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.     

Decision support for AIDS control programmes in Eastern Africa

The Acquired Immune Deficiency Syndrome (AIDS) constitutes the worst hazard to health care systems in Eastern Africa. Misallocation of scarce resources of AIDS Control Programmes will unavoidably lead to additional infections and casualties. The following paper discusses a system dynamics model which allows to assess the impact of different interventions on a pattern population in Eastern Africa. It becomes obvious that short- and long-term consequences of these programmes differ significantly. The optimal allocation of resources, therefore, is highly complex and calls for decision support systems to sustain AIDS control programmes.     

Elementary School Children's Ability to Distinguish Hypothetical Beliefs From Statements of Preference.

The authors examined students' understanding of hypotheses as beliefs that can be empirically verified. Thirty second graders and 30 sixth graders considered cases of disagreement about foods and colors that reflected either alternative hypotheses or different preferences. Their task was to decide whether the validity of each expressed belief could be determined and to justify their decision. Younger students considered both hypotheses and preferences as empirically verifiable, whereas older students were better able to recognize in some cases that preferences are legitimately variable. This lack of distinction may reflect limited metaconceptual ability or a deterministic epistemological view, both of which might interfere with the understanding of the hypothesis-testing process. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Persistence of conditioned flavor preferences is not due to inadvertent food reinforcement.

Almond preferences were produced by giving rats a mixture of almond and sucrose (Experiments 1-4) or saccharin (Experiment 4). A subsequent extinction procedure consisted of either repeated 2-bottle almond versus water tests (Experiment 1) or repeated exposure to almond alone (Experiments 2-4). The main independent variable was whether access to food following a session was given immediately, 30 min later, or 120 min later. No effect of extinction was found in any experiment. An important finding was that varying the delay until food access had no detectable effect. It was concluded that inadvertent flavor-food associations do not maintain preference for the flavor under extinction conditions. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

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.