This paper1 addresses different theoretical frameworks of organizational learning （OL） from two aspects： from the perspective of individuals to organizations and from the perspective of organizations to individuals...This paper1 addresses different theoretical frameworks of organizational learning （OL） from two aspects： from the perspective of individuals to organizations and from the perspective of organizations to individuals. The most significant finding is intended to highlight the guidelines for each of researchers＇ concentrated cluster and to demonstrate that different researchers present different guidelines for processes, individual skills, and changes in the environment, teamwork, and competitiveness. The insight, gained by considering OL as a process, is not routine It allows one to create, acquire, and transfer knowledge. This will always be limited to the internal capabilities developed during the course of the timeline and will identify skills and competencies generated in accordance with the requirements presented by different environments. OL is associated with both the change in organizational behaviors and the creation of a knowledge base.展开更多
An extended self-organizing map for supervised classification is proposed in this paper. Unlike other traditional SOMs, the model has an input layer, a Kohonen layer, and an output layer. The number of neurons in the ...An extended self-organizing map for supervised classification is proposed in this paper. Unlike other traditional SOMs, the model has an input layer, a Kohonen layer, and an output layer. The number of neurons in the input layer depends on the dimensionality of input patterns. The number of neurons in the output layer equals the number of the desired classes. The number of neurons in the Kohonen layer may be a few to several thousands, which depends on the complexity of classification problems and the classification precision. Each training sample is expressed by a pair of vectors ： an input vector and a class codebook vector. When a training sample is input into the model, Kohonen＇s competitive learning rule is applied to selecting the winning neuron from the Kohouen layer and the weight coefficients connecting all the neurons in the input layer with both the winning neuron and its neighbors in the Kohonen layer are modified to be closer to the input vector, and those connecting all the neurons around the winning neuron within a certain diameter in the Kohonen layer with all the neurons in the output layer are adjusted to be closer to the class codebook vector. If the number of training sam- ples is sufficiently large and the learning epochs iterate enough times, the model will be able to serve as a supervised classifier. The model has been tentatively applied to the supervised classification of multispectral remotely sensed data. The author compared the performances of the extended SOM and BPN in remotely sensed data classification. The investigation manifests that the extended SOM is feasible for supervised classification.展开更多
This paper presents a new discriminative approach for training Gaussian mixture models(GMMs)of hidden Markov models(HMMs)based acoustic model in a large vocabulary continuous speech recognition(LVCSR)system.This appro...This paper presents a new discriminative approach for training Gaussian mixture models(GMMs)of hidden Markov models(HMMs)based acoustic model in a large vocabulary continuous speech recognition(LVCSR)system.This approach is featured by embedding a rival penalized competitive learning(RPCL)mechanism on the level of hidden Markov states.For every input,the correct identity state,called winner and obtained by the Viterbi force alignment,is enhanced to describe this input while its most competitive rival is penalized by de-learning,which makes GMMs-based states become more discriminative.Without the extensive computing burden required by typical discriminative learning methods for one-pass recognition of the training set,the new approach saves computing costs considerably.Experiments show that the proposed method has a good convergence with better performances than the classical maximum likelihood estimation(MLE)based method.Comparing with two conventional discriminative methods,the proposed method demonstrates improved generalization ability,especially when the test set is not well matched with the training set.展开更多
This paper examines the Finnish basic education model,renowned for its student-centric approach and high performance in international assessments.The study explores the model’s core principles,including equity,teache...This paper examines the Finnish basic education model,renowned for its student-centric approach and high performance in international assessments.The study explores the model’s core principles,including equity,teacher autonomy,and minimal standardized testing,and their impact on student happiness and motivation.Through case studies,interviews,and surveys with students,teachers,and parents,the paper provides an in-depth analysis of the Finnish model’s effectiveness.Challenges such as adaptability to diverse cultural contexts,integration of immigrant students,and sustainability in the face of global educational trends are also discussed.The paper concludes with recommendations for the continued evolution of the Finnish model,emphasizing the need for adaptability,inclusivity,and a focus on sustainability and technology integration.展开更多
文摘This paper1 addresses different theoretical frameworks of organizational learning （OL） from two aspects： from the perspective of individuals to organizations and from the perspective of organizations to individuals. The most significant finding is intended to highlight the guidelines for each of researchers＇ concentrated cluster and to demonstrate that different researchers present different guidelines for processes, individual skills, and changes in the environment, teamwork, and competitiveness. The insight, gained by considering OL as a process, is not routine It allows one to create, acquire, and transfer knowledge. This will always be limited to the internal capabilities developed during the course of the timeline and will identify skills and competencies generated in accordance with the requirements presented by different environments. OL is associated with both the change in organizational behaviors and the creation of a knowledge base.
基金Supported by National Natural Science Foundation of China (No. 40872193)
文摘An extended self-organizing map for supervised classification is proposed in this paper. Unlike other traditional SOMs, the model has an input layer, a Kohonen layer, and an output layer. The number of neurons in the input layer depends on the dimensionality of input patterns. The number of neurons in the output layer equals the number of the desired classes. The number of neurons in the Kohonen layer may be a few to several thousands, which depends on the complexity of classification problems and the classification precision. Each training sample is expressed by a pair of vectors ： an input vector and a class codebook vector. When a training sample is input into the model, Kohonen＇s competitive learning rule is applied to selecting the winning neuron from the Kohouen layer and the weight coefficients connecting all the neurons in the input layer with both the winning neuron and its neighbors in the Kohonen layer are modified to be closer to the input vector, and those connecting all the neurons around the winning neuron within a certain diameter in the Kohonen layer with all the neurons in the output layer are adjusted to be closer to the class codebook vector. If the number of training sam- ples is sufficiently large and the learning epochs iterate enough times, the model will be able to serve as a supervised classifier. The model has been tentatively applied to the supervised classification of multispectral remotely sensed data. The author compared the performances of the extended SOM and BPN in remotely sensed data classification. The investigation manifests that the extended SOM is feasible for supervised classification.
基金The work was supported in part by the National Natural Science Foundation of China(Grant No.90920302)the National Key Basic Research Program of China(No.2009CB825404)+2 种基金the HGJ Grant(No.2011ZX01042-001-001)a research program from Microsoft China,and by a GRF grant from the Research Grant Council of Hong Kong SAR(CUHK 4180/10E)Lei XU is also supported by Chang Jiang Scholars Program,Chinese Ministry of Education for Chang Jiang Chair Professorship in Peking University.
文摘This paper presents a new discriminative approach for training Gaussian mixture models(GMMs)of hidden Markov models(HMMs)based acoustic model in a large vocabulary continuous speech recognition(LVCSR)system.This approach is featured by embedding a rival penalized competitive learning(RPCL)mechanism on the level of hidden Markov states.For every input,the correct identity state,called winner and obtained by the Viterbi force alignment,is enhanced to describe this input while its most competitive rival is penalized by de-learning,which makes GMMs-based states become more discriminative.Without the extensive computing burden required by typical discriminative learning methods for one-pass recognition of the training set,the new approach saves computing costs considerably.Experiments show that the proposed method has a good convergence with better performances than the classical maximum likelihood estimation(MLE)based method.Comparing with two conventional discriminative methods,the proposed method demonstrates improved generalization ability,especially when the test set is not well matched with the training set.
文摘This paper examines the Finnish basic education model,renowned for its student-centric approach and high performance in international assessments.The study explores the model’s core principles,including equity,teacher autonomy,and minimal standardized testing,and their impact on student happiness and motivation.Through case studies,interviews,and surveys with students,teachers,and parents,the paper provides an in-depth analysis of the Finnish model’s effectiveness.Challenges such as adaptability to diverse cultural contexts,integration of immigrant students,and sustainability in the face of global educational trends are also discussed.The paper concludes with recommendations for the continued evolution of the Finnish model,emphasizing the need for adaptability,inclusivity,and a focus on sustainability and technology integration.
