Titlebook: New Learning Paradigms in Soft Computing; Lakhmi C. Jain,Janusz Kacprzyk Book 2002 Springer-Verlag Berlin Heidelberg 2002 Lazy learning.ar

forebear

An Integer Programming Approach to Inductive Learning Using Genetic and Greedy Algorithms,ging to a class and do not describe most of the examples not belonging to this class. A pre-analysis of data is included that assigns higher weights to those values of attributes which occur more often in the positive than in the negative examples. The inductive learning problem is represented as a

沖突

Using Unlabeled Data for Learning Classification Problems,f the chapter, an approach of using both labeled and unlabeled data to train a multilayer perceptron is presented. The approach banks on the assumption that regions of low pattern density usually separate data classes. The unlabeled data are iteratively preprocessed by a perceptron being trained to

Diskectomy

Problems of Rule Induction from Preterm Birth Data,damental neural architectures, learning strategies and interpretation of their results are presented. We promote a concept of embedding principle: an original Boolean problem is represented in the language of fuzzy sets, afterwards solved through learning, and, finally, the result of learning re-int

Infect

Reduction of Discriminant Rules Based on Frequent Item Set Calculation, reducing the number of attributes in rules using frequent item sets calculation. The method is based in a basic step model. In our approach algorithms are divided in atomic operations that have been called basic steps so that it is easier to optimize the execution of any algorithm. We also present

overrule

Deriving a Concise Description of Non-Self Patterns in an Artificial Immune System,uses learning and memory when solving particular tasks. The learning process does not require negative examples and the acquired knowledge is represented in explicit form. The main actors of the immune systems are lymphocytes equipped with a set of receptors recognizing intruders, or pathogens (i.e.

Indent

Studies in Fuzziness and Soft Computinghttp://image.papertrans.cn/n/image/665427.jpg

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commensurate

Lazy Learning: A Logical Method for Supervised Learning,ction over the whole input domain. What makes global modeling appealing is the nice property that even for huge datasets, a parametric model can be stored in a small memory. Also, the evaluation of the parametric model requires a short program that can be executed in a reduced amount of time.

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Lakhmi C. Jain,Janusz KacprzykShows the use of fuzzy logic, neural networks and evoluationary computations in various machine learning procedures.Presents new trends in machine learning