Congenital cataract(CC)is one of the most common causes of pediatric visual impairment.As our understanding of CC's etiology,clinical manifestations,and pathogenic genes deepens,various CC categorization systems b...Congenital cataract(CC)is one of the most common causes of pediatric visual impairment.As our understanding of CC's etiology,clinical manifestations,and pathogenic genes deepens,various CC categorization systems based on different classification criteria have been proposed.Regrettably,the application of the CC category in clinical practice and scientific research is limited.It is challenging to obtain precise information that could guide the timely treatment decision-making for pediatric cataract patients or predict their prognosis from a specific CC classification.This review aims to discuss the status quo of CC categorization systems and the potential directions for future research in this field,focusing on categorization principles and scientific application in clinical practice.Additionally,it aims to propose the potential directions for future research in this domain.展开更多
Deep learning has achieved excellent results in various tasks in the field of computer vision,especially in fine-grained visual categorization.It aims to distinguish the subordinate categories of the label-level categ...Deep learning has achieved excellent results in various tasks in the field of computer vision,especially in fine-grained visual categorization.It aims to distinguish the subordinate categories of the label-level categories.Due to high intra-class variances and high inter-class similarity,the fine-grained visual categorization is extremely challenging.This paper first briefly introduces and analyzes the related public datasets.After that,some of the latest methods are reviewed.Based on the feature types,the feature processing methods,and the overall structure used in the model,we divide them into three types of methods:methods based on general convolutional neural network(CNN)and strong supervision of parts,methods based on single feature processing,and meth-ods based on multiple feature processing.Most methods of the first type have a relatively simple structure,which is the result of the initial research.The methods of the other two types include models that have special structures and training processes,which are helpful to obtain discriminative features.We conduct a specific analysis on several methods with high accuracy on pub-lic datasets.In addition,we support that the focus of the future research is to solve the demand of existing methods for the large amount of the data and the computing power.In terms of tech-nology,the extraction of the subtle feature information with the burgeoning vision transformer(ViT)network is also an important research direction.展开更多
To deal with the problem that arises when the conventional fuzzy class-association method applies repetitive scans of the classifier to classify new texts,which has low efficiency, a new approach based on the FCR-tree...To deal with the problem that arises when the conventional fuzzy class-association method applies repetitive scans of the classifier to classify new texts,which has low efficiency, a new approach based on the FCR-tree（fuzzy classification rules tree）for text categorization is proposed.The compactness of the FCR-tree saves significant space in storing a large set of rules when there are many repeated words in the rules.In comparison with classification rules,the fuzzy classification rules contain not only words,but also the fuzzy sets corresponding to the frequencies of words appearing in texts.Therefore,the construction of an FCR-tree and its structure are different from a CR-tree.To debase the difficulty of FCR-tree construction and rules retrieval,more k-FCR-trees are built.When classifying a new text,it is not necessary to search the paths of the sub-trees led by those words not appearing in this text,thus reducing the number of traveling rules.Experimental results show that the proposed approach obviously outperforms the conventional method in efficiency.展开更多
The concept of word classes （parts of speech） has always generated controversy among linguists. The earlier Prescriptive and Descriptive Schools might have set the pace for this controversy but the present dilemma i...The concept of word classes （parts of speech） has always generated controversy among linguists. The earlier Prescriptive and Descriptive Schools might have set the pace for this controversy but the present dilemma is much deeper. Learners and even teachers are sometimes at quandary as to how to proof that a particular word belongs to a particular class. This is because a word may sometimes belong to several classes, in context as in the word ＂watch＂ which can belong to different classes. This paper therefore tries to provide answers to the problem of word class classification by using a morphological and syntactical evidence to prove that English words follow a particular range of inflections and belong to strictly ordered particular categories and do not change their class arbitrarily. This is in line with the natural perfect order of homogeneity in creation which precludes a specie from merging effectively with another specie without having to undergo some fundamental changes. Other variables were also looked into and it was concluded that teachers and learners as well, can rely on this sub-categorization approach as a reliable paradigm for their assumptions concerning word classes.展开更多
In this paper, we discuss several issues related to automated classification of web pages, especially text classification of web pages. We analyze features selection and categorization algorithms of web pages and give...In this paper, we discuss several issues related to automated classification of web pages, especially text classification of web pages. We analyze features selection and categorization algorithms of web pages and give some suggestions for web pages categorization.展开更多
This paper proposes a new approach of feature selection based on the independent measure between features for text categorization. A fundamental hypothesis that occurrence of the terms in documents is independent of e...This paper proposes a new approach of feature selection based on the independent measure between features for text categorization. A fundamental hypothesis that occurrence of the terms in documents is independent of each other, widely used in the probabilistic models for text categorization （TC）, is discussed. However, the basic hypothesis is incom plete for independence of feature set. From the view of feature selection, a new independent measure between features is designed, by which a feature selection algorithm is given to ob rain a feature subset. The selected subset is high in relevance with category and strong in independence between features, satisfies the basic hypothesis at maximum degree. Compared with other traditional feature selection method in TC （which is only taken into the relevance account）, the performance of feature subset selected by our method is prior to others with experiments on the benchmark dataset of 20 Newsgroups.展开更多
This paper summarizes several automatic text categorization algorithms in common use recently, analyzes and compares their advantages and disadvantages. It provides clues for making use of appropriate automatic classi...This paper summarizes several automatic text categorization algorithms in common use recently, analyzes and compares their advantages and disadvantages. It provides clues for making use of appropriate automatic classifying algorithms in different fields. Finally some evaluations and summaries of these algorithms are discussed, and directions to further research have been pointed out. Key words text categorization - naive bayes - KNN - SVM - neural network CLC number TP 391 Foundation item: Supported by the National Natural Science Foundation of China (70031010) and the Research Foundation of Beijing Institute of TechnologyBiography: SHI Yong-feng (1980-), male, Master candidate, research direction: web information mining.展开更多
The scientific evidence that climate is changing due to greenhouse gas emission is now incontestable, which may put many social, biological, and geophysical systems in the world at risk. In this paper, we first identi...The scientific evidence that climate is changing due to greenhouse gas emission is now incontestable, which may put many social, biological, and geophysical systems in the world at risk. In this paper, we first identified main risks induced from or aggravated by climate change. Then we categorized them applying a new risk categorization system brought forward by Renn in a framework of International Risk Governance Council. We proposed that ＂uncertainty＂ could be treated as the classification criteria. Based on this, we established a quantitative method with fuzzy set theory, in which ＂confidence＂ and ＂likelihood＂, the main quantitative terms for expressing uncertainties in IPCC, were used as the feature parameters to construct the fuzzy membership functions of four risk types. According to the maximum principle, most climate change risks identified were classified into the appropriate risk types. In the mean time, given that not all the quantitative terms are available, a qualitative approach was also adopted as a complementary classification method. Finally, we get the preliminary results of climate change risk categorization, which might iay the foundation for the future integrated risk management of climate change.展开更多
With the purpose of improving the accuracy of text categorization and reducing the dimension of the feature space,this paper proposes a two-stage feature selection method based on a novel category correlation degree(C...With the purpose of improving the accuracy of text categorization and reducing the dimension of the feature space,this paper proposes a two-stage feature selection method based on a novel category correlation degree(CCD)method and latent semantic indexing(LSI).In the first stage,a novel CCD method is proposed to select the most effective features for text classification,which is more effective than the traditional feature selection method.In the second stage,document representation requires a high dimensionality of the feature space and does not take into account the semantic relation between features,which leads to a poor categorization accuracy.So LSI method is proposed to solve these problems by using statistically derived conceptual indices to replace the individual terms which can discover the important correlative relationship between features and reduce the feature space dimension.Firstly,each feature in our algorithm is ranked depending on their importance of classification using CCD method.Secondly,we construct a new semantic space based on LSI method among features.The experimental results have proved that our method can reduce effectively the dimension of text vector and improve the performance of text categorization.展开更多
Former knowledge engineering research aimed at boosting automatic reasoning.However recent knowledge management research focused on promoting the knowledge sharing and reusing among the people.Because of the different...Former knowledge engineering research aimed at boosting automatic reasoning.However recent knowledge management research focused on promoting the knowledge sharing and reusing among the people.Because of the different aims between the two directions,former knowledge representation schemata,such as rule based representation,frame from knowledge engineering research does not fit to the current knowledge management scenarios.In this paper,for the purpose of building knowledge management systems for product design enterprises,knowledge items are classified into seven types based on the semantics of their usage.Then their representations are discussed respectively.Based on the above classification,a knowledge representation meta-model and a basic domain ontology reference model for cooperative knowledge management systems are put forward.The reference model is an abstraction that can be reused and extended in knowledge management systems of different enterprises.Finally,the patterns of knowledge acquisition processes in cooperative knowledge management scenarios of product design processes are studied.展开更多
To avoid the curse of dimensionality, text categorization （TC） algorithms based on machine learning （ML） have to use an feature selection （FS） method to reduce the dimensionality of feature space. Although havin...To avoid the curse of dimensionality, text categorization （TC） algorithms based on machine learning （ML） have to use an feature selection （FS） method to reduce the dimensionality of feature space. Although having been widely used, FS process will generally cause information losing and then have much side-effect on the whole performance of TC algorithms. On the basis of the sparsity characteristic of text vectors, a new TC algorithm based on lazy feature selection （LFS） is presented. As a new type of embedded feature selection approach, the LFS method can greatly reduce the dimension of features without any information losing, which can improve both efficiency and performance of algorithms greatly. The experiments show the new algorithm can simultaneously achieve much higher both performance and efficiency than some of other classical TC algorithms.展开更多
In order to provide predictable runtime performante for text categorization （TC） systems, an innovative system design method is proposed for soft real time TC systems. An analyzable mathematical model is established...In order to provide predictable runtime performante for text categorization （TC） systems, an innovative system design method is proposed for soft real time TC systems. An analyzable mathematical model is established to approximately describe the nonlinear and time-varying TC systems. According to this mathematical model, the feedback control theory is adopted to prove the system＇s stableness and zero steady state error. The experiments result shows that the error of deadline satisfied ratio in the system is kept within 4 of the desired value. And the number of classifiers can be dynamically adjusted by the system itself to save the computa tion resources. The proposed methodology enables the theo retical analysis and evaluation to the TC systems, leading to a high-quality and low cost implementation approach.展开更多
A study examining affective information processing in persons with Multiple Sclerosis and healthy adults was carried out. It was hypothesized that individual characteristics could modulate participants’ emotional cat...A study examining affective information processing in persons with Multiple Sclerosis and healthy adults was carried out. It was hypothesized that individual characteristics could modulate participants’ emotional categorization and reaction times for categorization decisions. For example, individuals with negative valenced emotional profile (e.g. anxious) should choose negative emotional alternatives faster and more frequently. Participants consisted of two different populations: 80 right-handed healthy French-speakers, and 40 right-handed French- speakers with multiple sclerosis. The results showed a positive correlation between high- level of negative emotional sensibility and emotional categorization (decision and decision speed) for affective information presented on the right-side of the screen. For all participants there were more frequent emotional choices and faster decisions for left-side presented emotional alternatives. It seems individuals’ emotional differences in general and in MS populations modulate hemispheric asymmetry of processing emotional judgments.展开更多
The ability of achieving a semantic understanding of workspaces is an important capability for mobile robot.A method is proposed to categorize different places in a typical indoor environment by using a Kinect sensors...The ability of achieving a semantic understanding of workspaces is an important capability for mobile robot.A method is proposed to categorize different places in a typical indoor environment by using a Kinect sensors for mobile robot exploration.At first,the invariant feature based images stitching approach is adopted to form a panoramic image according to Kinect visual information,and the translation between Kinect depth information and obstacle distance information is performed to obtain virtual LIDAR data.Then,the semantic classifier is designed by using convolutional neural networks(CNN)for indoor place eategorization based on Kinect visual observations with panoramic view.At last,a frontier-based exploration method is applied to carry out indoor autonomous exploration of mo-bile robots,which integrates the CNN-based categorization approach.The proposed method has been implemented and tested on a real robot,and experiment results demonstrate the approach effective-ness on solving the semantic categorization problem for mobile robot exploration.展开更多
This paper is intended to reveal the likelihood that conceptual categorization can be used to understand a text by reconstructing the semantic categories through which the author＇s meaning is conveyed, and proposes a...This paper is intended to reveal the likelihood that conceptual categorization can be used to understand a text by reconstructing the semantic categories through which the author＇s meaning is conveyed, and proposes an alternative way to look into reading comprehension. It is proposed that categorization can be taken as an alternative approach to second/foreign language reading instruction. That is, while reading comprehension is defined in terms of the ability to recognize the inclusion and membership properties of contextually determined semantic categories in a text, the learner needs to arrange the events, actions, or concepts into a structured unit, both horizontally and vertically. Categorization theory will be introduced in relation to Rosch famous studies （1973, 1975）, examples taken from a graded reader will be illustrated as how to identify items with category structure, and finally issues that are not addressed in this paper will be discussed.展开更多
A hierarchical system to perform automatic categorization and reorientation of images using content analysis is pre-sented. The proposed system first categorizes images to some a priori defined categories using rotati...A hierarchical system to perform automatic categorization and reorientation of images using content analysis is pre-sented. The proposed system first categorizes images to some a priori defined categories using rotation invariant features. At the second stage, it detects their correct orientation out of {0o, 90o, 180o, and 270o} using category specific model. The system has been specially designed for embedded devices applications using only low level color and edge features. Machine learning algorithms optimized to suit the embedded implementation like support vector machines (SVMs) and scalable boosting have been used to develop classifiers for categorization and orientation detection. Results are presented on a collection of about 7000 consumer images collected from open resources. The proposed system finds it applications to various digital media products and brings pattern recognition solutions to the consumer electronics domain.展开更多
In this paper, the role of rare or infrequent terms in enhancing the accuracy of English Text Categorization using Polynomial Networks (PNs) is investigated. To study the impact of rare terms in enhancing the accuracy...In this paper, the role of rare or infrequent terms in enhancing the accuracy of English Text Categorization using Polynomial Networks (PNs) is investigated. To study the impact of rare terms in enhancing the accuracy of PNs-based text categorization, different term reduction criteria as well as different term weighting schemes were experimented on the Reuters Corpus using PNs. Each term weighting scheme on each reduced term set was tested once keeping the rare terms and another time removing them. All the experiments conducted in this research show that keeping rare terms substantially improves the performance of Polynomial Networks in Text Categorization, regardless of the term reduction method, the number of terms used in classification, or the term weighting scheme adopted.展开更多
In current study, behavioral measures were conducted to investigate clothing color. The purpose was to focus on the rule that color brightness influencedpositive-negative emotional categorization. Results showed that ...In current study, behavioral measures were conducted to investigate clothing color. The purpose was to focus on the rule that color brightness influencedpositive-negative emotional categorization. Results showed that the effect of brightness on clothing color emotion categorization was significant. With the increase of brightness, the variation curve of positive emotion appears to be a “U-shaped”, whereas that of the negative emotion shows an upside down “U-shaped”. Compared with the low brightness colors, the emotion reaction to the high brightness colors was more positive;Most of the colors with different brightness scales were classified as positive emotions and the minors were classified as negative emotions;the positive colors could be done much faster than the negative ones.展开更多
Content Based Image Retrieval, CBIR, performed an automated classification task for a queried image. It could relieve a user from the laborious and time-consuming metadata assigning for an image while working on massi...Content Based Image Retrieval, CBIR, performed an automated classification task for a queried image. It could relieve a user from the laborious and time-consuming metadata assigning for an image while working on massive image collection. For an image, user’s definition or description is subjective where it could belong to different categories as defined by different users. Human based categorization and computer-based categorization might produce different results due to different categorization criteria that rely on dataset structure and the clustering techniques. This paper is aimed to exhibit an idea for planning the dataset structure and choosing the clustering algorithm for CBIR implementation. There are 5 sections arranged in this paper;CBIR and QBE concepts are introduced in Section 1, related image categorization research is listed in Section 2, the 5 type of image clustering are described in Section 3, comparative analysis in Section 4, and Section 5 conclude this study. Outcome of this paper will be benefiting CBIR developer for various applications.展开更多
基金supported by the Joint Funding Project of Municipal Schools(Colleges)of Science and Technology Program of Guangzhou,China(2023A03J0188)the Guangzhou Municipal Science and Technology Project(202201011815).
文摘Congenital cataract(CC)is one of the most common causes of pediatric visual impairment.As our understanding of CC's etiology,clinical manifestations,and pathogenic genes deepens,various CC categorization systems based on different classification criteria have been proposed.Regrettably,the application of the CC category in clinical practice and scientific research is limited.It is challenging to obtain precise information that could guide the timely treatment decision-making for pediatric cataract patients or predict their prognosis from a specific CC classification.This review aims to discuss the status quo of CC categorization systems and the potential directions for future research in this field,focusing on categorization principles and scientific application in clinical practice.Additionally,it aims to propose the potential directions for future research in this domain.
基金supported by the National Natural Science Foundation of China(61571453,61806218).
文摘Deep learning has achieved excellent results in various tasks in the field of computer vision,especially in fine-grained visual categorization.It aims to distinguish the subordinate categories of the label-level categories.Due to high intra-class variances and high inter-class similarity,the fine-grained visual categorization is extremely challenging.This paper first briefly introduces and analyzes the related public datasets.After that,some of the latest methods are reviewed.Based on the feature types,the feature processing methods,and the overall structure used in the model,we divide them into three types of methods:methods based on general convolutional neural network(CNN)and strong supervision of parts,methods based on single feature processing,and meth-ods based on multiple feature processing.Most methods of the first type have a relatively simple structure,which is the result of the initial research.The methods of the other two types include models that have special structures and training processes,which are helpful to obtain discriminative features.We conduct a specific analysis on several methods with high accuracy on pub-lic datasets.In addition,we support that the focus of the future research is to solve the demand of existing methods for the large amount of the data and the computing power.In terms of tech-nology,the extraction of the subtle feature information with the burgeoning vision transformer(ViT)network is also an important research direction.
基金The National Natural Science Foundation of China(No.60473045)the Technology Research Project of Hebei Province(No.05213573)the Research Plan of Education Office of Hebei Province(No.2004406)
文摘To deal with the problem that arises when the conventional fuzzy class-association method applies repetitive scans of the classifier to classify new texts,which has low efficiency, a new approach based on the FCR-tree（fuzzy classification rules tree）for text categorization is proposed.The compactness of the FCR-tree saves significant space in storing a large set of rules when there are many repeated words in the rules.In comparison with classification rules,the fuzzy classification rules contain not only words,but also the fuzzy sets corresponding to the frequencies of words appearing in texts.Therefore,the construction of an FCR-tree and its structure are different from a CR-tree.To debase the difficulty of FCR-tree construction and rules retrieval,more k-FCR-trees are built.When classifying a new text,it is not necessary to search the paths of the sub-trees led by those words not appearing in this text,thus reducing the number of traveling rules.Experimental results show that the proposed approach obviously outperforms the conventional method in efficiency.
文摘The concept of word classes （parts of speech） has always generated controversy among linguists. The earlier Prescriptive and Descriptive Schools might have set the pace for this controversy but the present dilemma is much deeper. Learners and even teachers are sometimes at quandary as to how to proof that a particular word belongs to a particular class. This is because a word may sometimes belong to several classes, in context as in the word ＂watch＂ which can belong to different classes. This paper therefore tries to provide answers to the problem of word class classification by using a morphological and syntactical evidence to prove that English words follow a particular range of inflections and belong to strictly ordered particular categories and do not change their class arbitrarily. This is in line with the natural perfect order of homogeneity in creation which precludes a specie from merging effectively with another specie without having to undergo some fundamental changes. Other variables were also looked into and it was concluded that teachers and learners as well, can rely on this sub-categorization approach as a reliable paradigm for their assumptions concerning word classes.
文摘In this paper, we discuss several issues related to automated classification of web pages, especially text classification of web pages. We analyze features selection and categorization algorithms of web pages and give some suggestions for web pages categorization.
基金Supported by the National Natural Science Foun-dation of China (60373066 ,60503020) the Outstanding Young Sci-entist’s Fund(60425206) Doctor Foundatoin of Nanjing Universityof Posts and Telecommunications (2003-02)
文摘This paper proposes a new approach of feature selection based on the independent measure between features for text categorization. A fundamental hypothesis that occurrence of the terms in documents is independent of each other, widely used in the probabilistic models for text categorization （TC）, is discussed. However, the basic hypothesis is incom plete for independence of feature set. From the view of feature selection, a new independent measure between features is designed, by which a feature selection algorithm is given to ob rain a feature subset. The selected subset is high in relevance with category and strong in independence between features, satisfies the basic hypothesis at maximum degree. Compared with other traditional feature selection method in TC （which is only taken into the relevance account）, the performance of feature subset selected by our method is prior to others with experiments on the benchmark dataset of 20 Newsgroups.
文摘This paper summarizes several automatic text categorization algorithms in common use recently, analyzes and compares their advantages and disadvantages. It provides clues for making use of appropriate automatic classifying algorithms in different fields. Finally some evaluations and summaries of these algorithms are discussed, and directions to further research have been pointed out. Key words text categorization - naive bayes - KNN - SVM - neural network CLC number TP 391 Foundation item: Supported by the National Natural Science Foundation of China (70031010) and the Research Foundation of Beijing Institute of TechnologyBiography: SHI Yong-feng (1980-), male, Master candidate, research direction: web information mining.
基金Under the auspices of National Science & Technology Pillar Program During the 11th Five-Year Plan Period (No 2006BAD20B05)
文摘The scientific evidence that climate is changing due to greenhouse gas emission is now incontestable, which may put many social, biological, and geophysical systems in the world at risk. In this paper, we first identified main risks induced from or aggravated by climate change. Then we categorized them applying a new risk categorization system brought forward by Renn in a framework of International Risk Governance Council. We proposed that ＂uncertainty＂ could be treated as the classification criteria. Based on this, we established a quantitative method with fuzzy set theory, in which ＂confidence＂ and ＂likelihood＂, the main quantitative terms for expressing uncertainties in IPCC, were used as the feature parameters to construct the fuzzy membership functions of four risk types. According to the maximum principle, most climate change risks identified were classified into the appropriate risk types. In the mean time, given that not all the quantitative terms are available, a qualitative approach was also adopted as a complementary classification method. Finally, we get the preliminary results of climate change risk categorization, which might iay the foundation for the future integrated risk management of climate change.
基金the National Natural Science Foundation of China(Nos.61073193 and 61300230)the Key Science and Technology Foundation of Gansu Province(No.1102FKDA010)+1 种基金the Natural Science Foundation of Gansu Province(No.1107RJZA188)the Science and Technology Support Program of Gansu Province(No.1104GKCA037)
文摘With the purpose of improving the accuracy of text categorization and reducing the dimension of the feature space,this paper proposes a two-stage feature selection method based on a novel category correlation degree(CCD)method and latent semantic indexing(LSI).In the first stage,a novel CCD method is proposed to select the most effective features for text classification,which is more effective than the traditional feature selection method.In the second stage,document representation requires a high dimensionality of the feature space and does not take into account the semantic relation between features,which leads to a poor categorization accuracy.So LSI method is proposed to solve these problems by using statistically derived conceptual indices to replace the individual terms which can discover the important correlative relationship between features and reduce the feature space dimension.Firstly,each feature in our algorithm is ranked depending on their importance of classification using CCD method.Secondly,we construct a new semantic space based on LSI method among features.The experimental results have proved that our method can reduce effectively the dimension of text vector and improve the performance of text categorization.
基金the National Natural Science Foundation of China(No.61375053)the National High Technology Research and Development Program(863)of China(No.2009AA04Z106)
文摘Former knowledge engineering research aimed at boosting automatic reasoning.However recent knowledge management research focused on promoting the knowledge sharing and reusing among the people.Because of the different aims between the two directions,former knowledge representation schemata,such as rule based representation,frame from knowledge engineering research does not fit to the current knowledge management scenarios.In this paper,for the purpose of building knowledge management systems for product design enterprises,knowledge items are classified into seven types based on the semantics of their usage.Then their representations are discussed respectively.Based on the above classification,a knowledge representation meta-model and a basic domain ontology reference model for cooperative knowledge management systems are put forward.The reference model is an abstraction that can be reused and extended in knowledge management systems of different enterprises.Finally,the patterns of knowledge acquisition processes in cooperative knowledge management scenarios of product design processes are studied.
文摘To avoid the curse of dimensionality, text categorization （TC） algorithms based on machine learning （ML） have to use an feature selection （FS） method to reduce the dimensionality of feature space. Although having been widely used, FS process will generally cause information losing and then have much side-effect on the whole performance of TC algorithms. On the basis of the sparsity characteristic of text vectors, a new TC algorithm based on lazy feature selection （LFS） is presented. As a new type of embedded feature selection approach, the LFS method can greatly reduce the dimension of features without any information losing, which can improve both efficiency and performance of algorithms greatly. The experiments show the new algorithm can simultaneously achieve much higher both performance and efficiency than some of other classical TC algorithms.
基金Supported by the National Natural Science Foun-dation of China (90104032) ,the National High-Tech Research andDevelopment Plan of China (2003AA1Z2090)
文摘In order to provide predictable runtime performante for text categorization （TC） systems, an innovative system design method is proposed for soft real time TC systems. An analyzable mathematical model is established to approximately describe the nonlinear and time-varying TC systems. According to this mathematical model, the feedback control theory is adopted to prove the system＇s stableness and zero steady state error. The experiments result shows that the error of deadline satisfied ratio in the system is kept within 4 of the desired value. And the number of classifiers can be dynamically adjusted by the system itself to save the computa tion resources. The proposed methodology enables the theo retical analysis and evaluation to the TC systems, leading to a high-quality and low cost implementation approach.
文摘A study examining affective information processing in persons with Multiple Sclerosis and healthy adults was carried out. It was hypothesized that individual characteristics could modulate participants’ emotional categorization and reaction times for categorization decisions. For example, individuals with negative valenced emotional profile (e.g. anxious) should choose negative emotional alternatives faster and more frequently. Participants consisted of two different populations: 80 right-handed healthy French-speakers, and 40 right-handed French- speakers with multiple sclerosis. The results showed a positive correlation between high- level of negative emotional sensibility and emotional categorization (decision and decision speed) for affective information presented on the right-side of the screen. For all participants there were more frequent emotional choices and faster decisions for left-side presented emotional alternatives. It seems individuals’ emotional differences in general and in MS populations modulate hemispheric asymmetry of processing emotional judgments.
基金Supported by the National Key Basic Research Program of China(No.2013CB035503)
文摘The ability of achieving a semantic understanding of workspaces is an important capability for mobile robot.A method is proposed to categorize different places in a typical indoor environment by using a Kinect sensors for mobile robot exploration.At first,the invariant feature based images stitching approach is adopted to form a panoramic image according to Kinect visual information,and the translation between Kinect depth information and obstacle distance information is performed to obtain virtual LIDAR data.Then,the semantic classifier is designed by using convolutional neural networks(CNN)for indoor place eategorization based on Kinect visual observations with panoramic view.At last,a frontier-based exploration method is applied to carry out indoor autonomous exploration of mo-bile robots,which integrates the CNN-based categorization approach.The proposed method has been implemented and tested on a real robot,and experiment results demonstrate the approach effective-ness on solving the semantic categorization problem for mobile robot exploration.
文摘This paper is intended to reveal the likelihood that conceptual categorization can be used to understand a text by reconstructing the semantic categories through which the author＇s meaning is conveyed, and proposes an alternative way to look into reading comprehension. It is proposed that categorization can be taken as an alternative approach to second/foreign language reading instruction. That is, while reading comprehension is defined in terms of the ability to recognize the inclusion and membership properties of contextually determined semantic categories in a text, the learner needs to arrange the events, actions, or concepts into a structured unit, both horizontally and vertically. Categorization theory will be introduced in relation to Rosch famous studies （1973, 1975）, examples taken from a graded reader will be illustrated as how to identify items with category structure, and finally issues that are not addressed in this paper will be discussed.
文摘A hierarchical system to perform automatic categorization and reorientation of images using content analysis is pre-sented. The proposed system first categorizes images to some a priori defined categories using rotation invariant features. At the second stage, it detects their correct orientation out of {0o, 90o, 180o, and 270o} using category specific model. The system has been specially designed for embedded devices applications using only low level color and edge features. Machine learning algorithms optimized to suit the embedded implementation like support vector machines (SVMs) and scalable boosting have been used to develop classifiers for categorization and orientation detection. Results are presented on a collection of about 7000 consumer images collected from open resources. The proposed system finds it applications to various digital media products and brings pattern recognition solutions to the consumer electronics domain.
文摘In this paper, the role of rare or infrequent terms in enhancing the accuracy of English Text Categorization using Polynomial Networks (PNs) is investigated. To study the impact of rare terms in enhancing the accuracy of PNs-based text categorization, different term reduction criteria as well as different term weighting schemes were experimented on the Reuters Corpus using PNs. Each term weighting scheme on each reduced term set was tested once keeping the rare terms and another time removing them. All the experiments conducted in this research show that keeping rare terms substantially improves the performance of Polynomial Networks in Text Categorization, regardless of the term reduction method, the number of terms used in classification, or the term weighting scheme adopted.
文摘In current study, behavioral measures were conducted to investigate clothing color. The purpose was to focus on the rule that color brightness influencedpositive-negative emotional categorization. Results showed that the effect of brightness on clothing color emotion categorization was significant. With the increase of brightness, the variation curve of positive emotion appears to be a “U-shaped”, whereas that of the negative emotion shows an upside down “U-shaped”. Compared with the low brightness colors, the emotion reaction to the high brightness colors was more positive;Most of the colors with different brightness scales were classified as positive emotions and the minors were classified as negative emotions;the positive colors could be done much faster than the negative ones.
文摘Content Based Image Retrieval, CBIR, performed an automated classification task for a queried image. It could relieve a user from the laborious and time-consuming metadata assigning for an image while working on massive image collection. For an image, user’s definition or description is subjective where it could belong to different categories as defined by different users. Human based categorization and computer-based categorization might produce different results due to different categorization criteria that rely on dataset structure and the clustering techniques. This paper is aimed to exhibit an idea for planning the dataset structure and choosing the clustering algorithm for CBIR implementation. There are 5 sections arranged in this paper;CBIR and QBE concepts are introduced in Section 1, related image categorization research is listed in Section 2, the 5 type of image clustering are described in Section 3, comparative analysis in Section 4, and Section 5 conclude this study. Outcome of this paper will be benefiting CBIR developer for various applications.
