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Iterative Single Data Algorithm for Training Kernel Machines from Huge Data Sets: Theory and Performance
The chapter introduces the latest developments and results of Iterative Single Data Algorithm (ISDA) for solving large-scale support vector machines...
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Application of Support Vector Machines in Inverse Problems in Ocean Color Remote Sensing
Neural networks are widely used as transfer functions in inverse problems in remote sensing. However, this method still suffers from some problems...
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Tachycardia Discrimination in Implantable Cardioverter Defibrillators Using Support Vector Machines and Bootstrap Resampling
Accurate automatic discrimination between supraventricular (SV) and ventricular (V) tachycardia (T) in implantable cardioverter defibrillators (ICD)...
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Cancer Diagnosis and Protein Secondary Structure Prediction Using Support Vector Machines
In this chapter, we use support vector machines (SVMs) to deal with two bioinformatics problems, i.e., cancer diagnosis based on gene expression data...
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The Kernel Memory Concept – A Paradigm Shift from Conventional Connectionism
In this chapter, the general concept of kernel memory (KM) is described, which is given as the basis for not only representing the general notion of...
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Modelling Abstract Notions Relevant to the Mind and the Associated Modules
This chapter is devoted to the remaining four modules within the AMS, i.e. 1) attention, 2) emotion, 3) intention, and 4) intuition module, and their...
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Granular Nested Causal Complexes
Causal reasoning occupies a central position in human reasoning. In many ways, causality is granular. This is true for: perception, commonsense...
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Using an Adapted Classification Based on Associations Algorithm in an Activity-Based Transportation System
A lot of research has been carried out in the past by using association rules to build more accurate classifiers. The idea behind these integrated...
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The Evolution of the Concept of Fuzzy Measure
Most information discovery processes need to understand the reasons of the success of the inference methods or the usability of the new information,...
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Discovering the Factors Affecting the Location Selection of FDI in China*
Since the late 1970s, Foreign Direct Investment (FDI) has played an important role in the economic development of China. However, the growth of FDI...
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Reconfiguration Using a Virtual Actuator
This chapter develops the concept of a virtual actuator. The idea of a virtual actuator is to use the input signal meant for the nominal process and...
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INTELLIGENT MUSICAL INSTRUMENT SOUND CLASSIFICATION
This chapter is devoted to intelligent classification of the sound of musical instruments. Although it is possible, and in some applications...
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INTRODUCTION
Over the last decade, a series of publications has brought and established new research areas related to music, and intensified the research verging...
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Kernel Discriminant Learning with Application to Face Recognition
When applied to high-dimensional pattern classification tasks such as face recognition, traditional kernel discriminant analysis methods often suffer...
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Multiple Model Estimation for Nonlinear Classification
This chapter describes a new method for nonlinear classification using a collection of several simple (linear) classifiers. The approach is based on...
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Active Support Vector Learning with Statistical Queries
The article describes an active learning strategy to solve the large quadratic programming (QP) problem of support vector machine (SVM) design in...
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Sequential Pattern Mining*
Sequential pattern discovery has emerged as an important research topic in knowledge discovery and data mining with broad applications. Previous...
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Uncertain Knowledge Association Through Information Gain
The problem of entity association is at the core of information mining techniques. In this work we propose an approach that links the similarity of...
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Clustering Via Decision Tree Construction
Clustering is an exploratory data analysis task. It aims to find the intrinsic structure of data by organizing data objects into similarity groups or...
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A New Theoretical Framework for K-Means-Type Clustering
One of the fundamental clustering problems is to assign n points into k clusters based on the minimal sum-of-squares(MSSC), which is known to be...