A hierarchical model for structure learning based on the physiological characteristics of neurons

Almost all applications of Artificial Neural Networks (ANNs) depend mainly on their memory ability. The characteristics of typical ANN models are fixed connections, with evolved weights, globalized representations, and globalized optimizations, all based on a mathematical approach. This makes those models to be deficient in robustness, efficiency of learning, capacity, anti-jamming between training sets, and correlativity of samples, etc. In this paper, we attempt to address these problems by adopting the characteristics of biological neurons in morphology and signal processing. A hierarchical neural network was designed and realized to implement structure learning and representations based on connected structures. The basic characteristics of this model are localized and random connections, field limitations of neuron fan-in and fan-out, dynamic behavior of neurons, and samples represented through different sub-circuits of neurons specialized into different response patterns. At the end of this paper, some important aspects of error correction, capacity, learning efficiency, and soundness of structural representation are analyzed theoretically. This paper has demonstrated the feasibility and advantages of structure learning and representation. This model can serve as a fundamental element of cognitive systems such as perception and associative memory.     

Review of The achievement test desk reference: A guide to learning disability identification, second edition.

Reviews the book, The achievement test desk reference: A guide to learning disability identification, second edition by Dawn P. Flanagan, Samuel O. Ortiz, Vincent C. Alfonso, and Jennifer T. Mascolo (2006). The Second Edition of the Achievement test desk reference comes only four years after its first edition. Its content echoes the first edition, with the chapters now re-organized and updated. However, this edition is also timely; its significant contributions are its theoretical and practical model for defining and assessing learning disabilities (LD) and its update of practical information for interpreting many of the new standardized achievement tests. The goal for this edition was to provide an integrated and systematic framework for achievement testing within the framework of the Cattell-Horn-Carroll (CHC) theory of human cognitive abilities and the Response to Intervention (RTI) movement. Part 1 of provides an update of the theories, definitions, assessment, interpretation, and identification of LD. Part 2 describes and reviews the psychometric, theoretical, and qualitative features of achievement tests published between 1996 and 2006 and normed on people from the United States. Part 3 provides a comprehensive, step-by-step process for assessing learning disabilities under the Individuals with Disabilities Education Improvement Act (IDEA 2004). This volume guides practitioners to choose the best achievement tests to use. It shows how to get the most efficient and effective evaluations of students' academic skills that could account for their difficulties and possible learning disabilities. This book will be essential reference material for effective LD assessments. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Localization of Function Within the Dorsal Hippocampus: The Role of the CA3 Subregion in Paired-Associate Learning.

Computational models and electrophysiological data suggest that the CA3 subregion of the hippocampus supports the formation of arbitrary associations; however, no behavioral studies have been conducted to test this hypothesis. Rats with neurotoxin-induced lesions of dorsal dentate gyrus (DG), CA3, or CA1 were tested on object-place and odor-place paired-associate tasks to test whether the mechanism that supports paired-associate learning is localized to the CA3 subregion of the dorsal hippocampus or whether all hippocampal subregions contribute to paired-associate learning. The data indicate that rats with DG or CA1 lesions learned the tasks as well as controls; however, CA3-lesioned rats were impaired in learning the tasks. Thus, the CA3 subregion of the dorsal hippocampus contains a mechanism to support paired-associate learning. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Three perspectives of data mining

This paper reviews three recent books on data mining written from three different perspectives, i.e., databases, machine learning, and statistics. Although the exploration in this paper is suggestive instead of conclusive, it reveals that besides some common properties, different perspectives lay strong emphases on different aspects of data mining. The emphasis of the database perspective is on efficiency because this perspective strongly concerns the whole discovery process and huge data volume. The emphasis of the machine learning perspective is on effectiveness because this perspective is heavily attracted by substantive heuristics working well in data analysis although they may not always be useful. As for the statistics perspective, its emphasis is on validity because this perspective cares much for mathematical soundness behind mining methods.     

网络电视的发展及其对远程教育的影响

计算机网络技术的发展给电视节目的播出方式带来了巨大的变化，网络电视这一传播媒体应运而生。从而改变了目前广播电视媒体信息单向传播的缺陷。介绍了网络电视的发展，就网络电视对远程教育的影响进行了探讨。     

Information-Based Evaluation Criterion for Classifier's Performance

In the past few years many systems for learning decision rules from examples were developed. As different systems allow different types of answers when classifying new instances, it is difficult to appropriately evaluate the systems' classification power in comparison with other classification systems or in comparison with human experts. Classification accuracy is usually used as a measure of classification performance. This measure is, however, known to have several defects. A fair evaluation criterion should exclude the influence of the class probabilities which may enable a completely uninformed classifier to trivially achieve high classification accuracy. In this paper a method for evaluating the information score of a classifier's answers is proposed. It excludes the influence of prior probabilities, deals with various types of imperfect or probabilistic answers and can be used also for comparing the performance in different domains.     

Self-learning inductive inference machines

Self-knowledge is a concept that is present in several philosophies. In this article, we consider the issue of whether or not a learning algorithm can in some sense possess self-knowledge. The question is answered affirmatively. Self-learning inductive inference algorithms are taken to be those that learn programs for their own algorithms, in addition to other functions.
La connaissance de soi est un concept qui se retrouve dans plusieurs philosophies. Dans cet article, les auteurs s'interrogent à savoir si un algorithme d' apprentissage peut dans une certaine mesure posséder la connaissance de soi. lis apportent une reponse positive a cette question. Les algorithmes d'inference inductive autodidactes sont ceux qui font l'apprentissage de programmes pour leurs propres algorithmes, en plus d' autres fonctions.     

PAC-learnability of probabilistic deterministic finite state automata in terms of variation distance

We consider the problem of PAC-learning distributions over strings, represented by probabilistic deterministic finite automata (PDFAs). PDFAs are a probabilistic model for the generation of strings of symbols, that have been used in the context of speech and handwriting recognition, and bioinformatics. Recent work on learning PDFAs from random examples has used the KL-divergence as the error measure; here we use the variation distance. We build on recent work by Clark and Thollard, and show that the use of the variation distance allows simplifications to be made to the algorithms, and also a strengthening of the results; in particular that using the variation distance, we obtain polynomial sample size bounds that are independent of the expected length of strings.     

Multiagent learning using a variable learning rate

Learning to act in a multiagent environment is a difficult problem since the normal definition of an optimal policy no longer applies. The optimal policy at any moment depends on the policies of the other agents. This creates a situation of learning a moving target. Previous learning algorithms have one of two shortcomings depending on their approach. They either converge to a policy that may not be optimal against the specific opponents' policies, or they may not converge at all. In this article we examine this learning problem in the framework of stochastic games. We look at a number of previous learning algorithms showing how they fail at one of the above criteria. We then contribute a new reinforcement learning technique using a variable learning rate to overcome these shortcomings. Specifically, we introduce the WoLF principle, “Win or Learn Fast”, for varying the learning rate. We examine this technique theoretically, proving convergence in self-play on a restricted class of iterated matrix games. We also present empirical results on a variety of more general stochastic games, in situations of self-play and otherwise, demonstrating the wide applicability of this method.     

基于约束优化的联想记忆模型学习算法

本文提出了一种对称互连神经元网络的学习策略,利用全局约束优化方法确定连接权。优化过程采用了梯度下降技术。这种学习算法可以保证训练样本成为系统的稳定吸引子,并且具有优化意义上的最大吸引域。本文讨论了网络的存储容量,训练样本的渐近稳定性和吸引域大小。计算机实验结果说明了学习算法的优越性。