This paper addresses the high dimension sample problem in discriminate analysis under nonparametric and supervised assumptions. Since there is a kind of equivalence between the probabilistic dependence measure and the...This paper addresses the high dimension sample problem in discriminate analysis under nonparametric and supervised assumptions. Since there is a kind of equivalence between the probabilistic dependence measure and the Bayes classification error probability, we propose to use an iterative algorithm to optimize the dimension reduction for classification with a probabilistic approach to achieve the Bayes classifier. The estimated probabilities of different errors encountered along the different phases of the system are realized by the Kernel estimate which is adjusted in a means of the smoothing parameter. Experiment results suggest that the proposed approach performs well.展开更多
We present a scheme for realizing probabilistic teleportation of an unknown N-atom state via cavity QED. This scheme requires only a nonmaximally entangled pair to be used as a quantum channel, so the requirement of e...We present a scheme for realizing probabilistic teleportation of an unknown N-atom state via cavity QED. This scheme requires only a nonmaximally entangled pair to be used as a quantum channel, so the requirement of entanglement is reduced. In addition, our scheme does not involve the Bell-state measurement and is insensitive to the cavity decay, which is important from the experimental point of view. If the quantum channel is a two-atom maximally entangled state, teleportation of an unknown N-atom state can be realized by a simpler scheme via cavity QED.展开更多
文摘This paper addresses the high dimension sample problem in discriminate analysis under nonparametric and supervised assumptions. Since there is a kind of equivalence between the probabilistic dependence measure and the Bayes classification error probability, we propose to use an iterative algorithm to optimize the dimension reduction for classification with a probabilistic approach to achieve the Bayes classifier. The estimated probabilities of different errors encountered along the different phases of the system are realized by the Kernel estimate which is adjusted in a means of the smoothing parameter. Experiment results suggest that the proposed approach performs well.
基金Project supported by the National Natural Science Foundation of China （Grant No 10574022）, and the Funds of the Natural Science of Fujian Province, China （Grant No Z0512006）.
文摘We present a scheme for realizing probabilistic teleportation of an unknown N-atom state via cavity QED. This scheme requires only a nonmaximally entangled pair to be used as a quantum channel, so the requirement of entanglement is reduced. In addition, our scheme does not involve the Bell-state measurement and is insensitive to the cavity decay, which is important from the experimental point of view. If the quantum channel is a two-atom maximally entangled state, teleportation of an unknown N-atom state can be realized by a simpler scheme via cavity QED.
