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.     

The time course of spoken word learning and recognition: Studies with artificial lexicons.

The time course of spoken word recognition depends largely on the frequencies of a word and its competitors, or neighbors (similar-sounding words). However, variability in natural lexicons makes systematic analysis of frequency and neighbor similarity difficult. Artificial lexicons were used to achieve precise control over word frequency and phonological similarity. Eye tracking provided time course measures of lexical activation and competition (during spoken instructions to perform visually guided tasks) both during and after word learning, as a function of word frequency, neighbor type, and neighbor frequency. Apparent shifts from holistic to incremental competitor effects were observed in adults and neural network simulations, suggesting such shifts reflect general properties of learning rather than changes in the nature of lexical representations. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

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。     

支持向量机在交通量预测中的应用

交通量预测对于区域交通规划有重要意义。提出一种基于支持向量机理论的交通量预测方法。该方法以统计学习理论为基础，通过和BP神经网络进行比较的实验，证明其在交通量预测中的有效性。     

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.