RATIONALITY AND ITS ROLES IN REASONING

The economic theory of rationality promises to equal mathematical logic in its importance for the mechanization of reasoning. We survey the growing literature on how the basic notions of probability, utility, and rational choice, coupled with practical limitations on information and resources, influence the design and analysis of reasoning and representation systems.     

面向对象的思想方法在决策支持系统中建立模型库的应用

本文介绍了面向对象程序设计的基本概念，思想和方法；     

NOVELTY DETECTION AND IN-LINE LEARNING OF NOVEL CONCEPTS ACCORDING TO A CASE-BASED REASONING PROCESS SCHEMA FOR HIGH-CONTENT IMAGE ANALYSIS IN SYSTEM BIOLOGY AND MEDICINE

Novelty detection, the ability to identify new or unknown situations that were never experienced before, is useful for intelligent systems aspiring to operate in environments where data are acquired incrementally. This characteristic is common to numerous problems in medical diagnosis and visual perception. We propose to see novelty detection as a case-based reasoning (CBR) process. Our novelty-detection method is able to detect the novel situation as well as to use the novel events for immediate reasoning. To ensure this capacity we combine statistical and similarity inference and learning. This view of CBR takes into account the properties of data such as the uncertainty and the underlying concepts such as storage, learning, retrieval and indexing can be formalized and performed efficiently. The novel events can be items being outliers or items making up a new class. Storage is done in a structured fashion, so that they can give a strategy for controlling the statistical learning process. Only when enough samples are available for a group of novel events, the statistical learning process proves if new models should be learned or if old models should be reorganized according to the new data. We use our novelty detection and handling method in a scenario where cell images may vary according to the particular cell line and the varying image quality given by the image acquisition unit. This scenario is valid for many medical applications and applications in system biology.     

Web站点从NT到UNIX迁移中的问题及其解决方法

本文主要阐述了目前Ｗｅｂ站点在从ＷｉｎｄｏｗｓＮＴ平台到ＵＮＩＸ平台迁移的过程中的存在问题及解决方法。     

示例学习的最大复合问题及算法

本文证明了示例学习中的最大复合问题是ＮＰ难题，给出了求解最大复问题的近似算法，并将此示例学习算法应用于手写数学识别。实验证明，基于最大复合的学习算法和ＡＱ１５相比，速度快，得到的公式少、匹配精度高。