State Sampling Dependence of Hopfield Network Inference  

作　　者：HUANG Hal-Ping 黄海平(Key Laboratory of Frontiers in Theoretical Physics,Institute of Theoretical Physics,Chinese Academy of Sciences,Beijing 100190,China;Department of Physics,Hong Kong University of Science and Technology,Hong Kong,China)

机构地区：[1]Key Laboratory of Frontiers in Theoretical Physics,Institute of Theoretical Physics,Chinese Academy of Sciences,Beijing 100190,China [2]Department of Physics,Hong Kong University of Science and Technology,Hong Kong,China

出　　处：《Communications in Theoretical Physics》2012年第1期169-172,共4页理论物理通讯（英文版）

基　　金：Supported by the National Science Foundation of China under Grant Nos. 10774150,10834014;the China 973-Program under Grant Nos. 2007CB935903 and HKUST605010

摘　　要：The fully connected Hopfield network is inferred based on observed magnetizations and pairwise correlations.We present the system in the glassy phase with low temperature and high memory load.We find that the inference error is very sensitive to the form of state sampling.When a single state is sampled to compute magnetizations and correlations,the inference error is almost indistinguishable irrespective of the sampled state.However,the error can be greatly reduced if the data is collected with state transitions.Our result holds for different disorder samples and accounts for the previously observed large fluctuations of inference error at low temperatures.

关 键 词：INFERENCE Hopfield network spin glass 

分 类 号：O157.5[理学—数学]