基于支持向量机的多类分类研究

现今流行的分类方法的重要基础是传统的统计学，前提是要有足够的样本，当样本数目有限时容易出现过学习的问题，导致分类效果不理想。引入支持向量机方法，它基于统计学习理论，采用了结构风险最小化原则代替经验风险最小化原则，较好的解决了小样本学习的问题；又由于采用了核函数思想，把非线性空间的问题转换到线性空间，降低了算法的复杂度。对其相关内容包括优化算法及多类分类问题的解决进行了研究，最后用一个实例说明了该方法的可行性和有效性。     

An intelligent web-page classifier with fair feature-subset selection

The explosion of on-line information has given rise to many manually constructed topic hierarchies (such as Yahoo!!). But with the current growth rate in the amount of information, manual classification in topic hierarchies results in an immense information bottleneck. Therefore, developing an automatic classifier is an urgent need. However, classifiers suffer from enormous dimensionality, since the dimensionality is determined by the number of distinct keywords in a document corpus. More seriously, most classifiers are either working slowly or they are constructed subjectively without any learning ability. In this paper, we address these problems with a fair feature-subset selection (FFSS) algorithm and an adaptive fuzzy learning network (AFLN) for classification. The FFSS algorithm is used to reduce the enormous dimensionality. It not only gives fair treatment to each category but also has ability to identify useful features, including both positive and negative features. On the other hand, the AFLN provides extremely fast learning ability to model the uncertain behavior for classification so as to correct the fuzzy matrix automatically. Experimental results show that both FFSS algorithm and the AFLN lead to a significant improvement in document classification, compared to alternative approaches.     

Preschool Competency in Context: An Investigation of the Unique Contribution of Child Competencies to Early Academic Success.

The present concurrent study combined developmental and ecological considerations to examine the unique contribution of multiple preschool competencies to an indicator of early academic success. Participants included 195 Head Start children from 32 classrooms representative of a large, urban Head Start program. Dimensional (variable-centered) analyses revealed 3 distinct classroom competency dimensions (i.e., General Classroom Competencies, Specific Approaches to Learning, and Interpersonal Classroom Behavioral Problems). The first 2 of these dimensions were found to be uniquely associated with early academic success. Findings from typological (person-centered) analyses supported the dimensional findings. Typological analyses revealed 7 profiles of classroom competency distinguished by high scores on the dimensions of General Competencies and Approaches to Learning, and these profiles were found to relate differentially to the indicator of early academic success. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

A Revised Methodology for Research on Metamemory: Pre-judgment Recall And Monitoring (PRAM).

A revised methodology is described for research on metacognitive monitoring, especially judgments of learning (JOLs), to investigate psychological processing that previously has been only hypothetical and unobservable. During data collection a new stage of recall occurs just prior to the JOL, so that during data analysis the items can be partitioned into subcategories to measure the degree of JOL accuracy in ways that are more analytic than was previously possible. A weighted-average combinatorial rule allows the component measures of JOL accuracy to be combined into the usual overall measure of metacognitive accuracy. An example using the revised methodology offers a new explanation for the delayed-JOL effect, in which delayed JOLs are more accurate than immediate JOLs for predicting recall. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Artificial neural networks for coordination and control: The portability of experiential representations

It is time to locate connectionist representation theory in the new wave of robotics research. The utility of representations developed in artificial neural networks (ANNs) during learning has been demonstrated in cognitive science research since the 1980s. The research reported here puts learned representations to work in a decentered control task, the disembodied arm problem, in which a mobile robot operates an arm fixed to a table to pick up objects. There is no physical linkage between the arm and the robot and so the robot's point of view must be decentered. This is done by developing a modular Artificial Neural Net system in three stages: (i) a classifier net is trained with laser scan data to output transformationally invariant position classes; (ii) an arm net is trained for picking up objects; (iii) an inter net is trained to communicate and coordinate the sensing and acting. The completed system is shown to create new nonsymbolic transformationally invariant representations in order to perform the effective generalization of decentered viewpoints.     

使用遗传算法的迷宫学习

本文试用遗传计算及决策制定方法来实现机器人有认识迷宫意向的能力，这里“意向”是指遵循对迷宫特有的路径规则所出的决定，令机器人在两个相对的迷宫中行走学习，一个迷宫路径是基于同样的意向，另一个则基于不同的意向，研究结果确认了已达成的意向学习。     

大规模数据库中的知识获取

一、前言数据是知识的潭泉,拥有大量的数据与拥有许多有用的知识完全是两回事.为了有效地利用大量的公共数据,必须更好地理解这些数据,并从其中快速、准确地发现知识.这里所说的知识是指大量数据中存在的规律性(r egularity)或不同属性值之间所存在的[I F THEN〕规则.将所获取的知识附加于仅由事实数据(fact data)构成的传统数据库上,既可强化数据库的查询能力,又可给数据库提供推理能力,     

空间机器人遥控、自主与协同操作的软件基础

空间机器人控制语言是实现空间机器人三种控制方式:遥控操作,自主操作和协同操作的软件基础,本文描述了该语言的基本结构,对于自主方式,给出了编程示例;对于遥控方式,运用程序辅助的方法,解决了操作员单独进行主/从操作时难以解决的问题.     

粉喷桩处理软弱地基在祁临高速公路的应用

结合工程实例，介绍了粉喷桩的所属类型，粉喷桩对软弱地基加固的原因，所适应土质及加固特点，阐述了祁临十八合同段的粉喷桩施工过程及施工中应注意的若干问题，得出粉喷桩处理不良地基效果良好。     

Design and Implementation of a Flexible Manufacturing Control System Using Neural Network

Design and implementation of a sequential controller based on the concept of artificial neural networks for a flexible manufacturing system are presented. The recurrent neural network (RNN) type is used for such a purpose. Contrary to the programmable controller, an RNN-based sequential controller is based on a definite mathematical model rather than depending on experience and trial and error techniques. The proposed controller is also more flexible because it is not limited by the restrictions of the finite state automata theory. Adequate guidelines of how to construct an RNN-based sequential controller are presented. These guidelines are applied to different case studies. The proposed controller is tested by simulations and real-time experiments. These tests prove the successfulness of the proposed controller performances. Theoretical as well as experimental results are presented and discussed indicating that the proposed design procedure using Elman's RNN can be effective in designing a sequential controller for event-based type manufacturing systems. In addition, the simulation results assure the effectiveness of the proposed controller to outperform the effect of noisy inputs.