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Active-Set Methods for Support Vector Machines
This chapter describes an active-set algorithm for quadratic programming problems that arise from the computation of support vector machines (SVMs)....
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Conclusions
This chapter concludes this monograph. It starts with the summary of the progress, results, and status of the research project, followed by tasks of...
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A First Improvement: Using Promoters
Harik [47] took Holland’s call [53] for evolution of tight genetic linkage and proposed the linkage learning genetic algorithm (LLGA), which used a...
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Logical Regression Analysis: From Mathematical Formulas to Linguistic Rules
Data mining means the discovery of knowledge from (a large amount of)data, and so data mining should provide not only predictions but also knowledge...
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Mining Small Objects in Large Images Using Neural Networks
Since the late 1980s, neural networks have been widely applied to data mining. However, they are often criticised and regarded as a “black box” due...
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An Alternative Approach to Mining Association Rules
An alternative approach to mining association rules is presented. It is based on representation of analysed data by suitable strings of bits. This...
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Designing Robust Regression Models
In this study we focus on the preference among competing models from a family of polynomial regressors. Classical statistics offers a number of...
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Reporting Data Mining Results in a Natural Language
An attempt to report results of data mining in automatically generated natural language sentences is described. Several types of association rules...
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Comparative Study of Sequential Pattern Mining Models
The process of finding interesting, novel, and useful patterns from data is now commonly known as Knowledge Discovery and Data mining (KDD). In this...
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Decision Making Based on Hybrid of Multi-Knowledge and Naïve Bayes Classifier
In general, knowledge can be represented by a map** from a hypothesis space to a decision space. Usually, multiple map**s can be obtained from an...
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Learning in the AMS Context
In this chapter, we dig further into the notion of “learning” within the AMS context. In conventional connectionist models, the term “learning” is...
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Convergence Time for the Linkage Learning Genetic Algorithm
As indicated in the previous chapter, inspired by the coding mechanism existing in genetics, introducing the use of promoters in the linkage learning...
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Introducing Subchromosome Representations
While the linkage learning genetic algorithm achieved successful genetic linkage learning on problems with badly scaled building blocks, it was less...
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COGNITIVE PROCESSING IN ACOUSTICS
The idea of vagueness (contrary to bi-valent logic) appeared at the end of the 19th century, and was formally applied to the field of logic in 1923...
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Posting Act Tagging Using Transformation-Based Learning
In this article we present the application of transformation-based learning (TBL) [1] to the task of assigning tags to postings in online chat...
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Direct Mining of Rules from Data with Missing Values
The paper presents an approach to and technique for direct mining of binary data with missing values aiming at extraction of classification rules,...
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Fuzzy Rules Extraction from Connectionist Structures
In the conjugate effort of building shells for Hybrid Intelligent Systems with a homogenous architecture, based on neural networks, a difficult task...
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Call Center Model
The queuing system in this chapter is shown in Fig. 10.1. This application was adopted from an example in [1].
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Hierarchical BOA in the Real World
The last chapter designed hBOA, which was shown to provide scalable solution for hierarchical traps. Since hierarchical traps were designed to test...
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Introduction to Pattern Recognition with Intelligent Systems
We describe in this book, new methods for intelligent pattern recognition using soft computing techniques. Soft Computing (SC) consists of several...