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" Machine learning research progress "
Hannah Peters and Mia Vogel, editors.
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BL
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| Record Number
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761658
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b581633
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| Main Entry
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Hannah Peters and Mia Vogel, editors.
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| Title & Author
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Machine learning research progress\ Hannah Peters and Mia Vogel, editors.
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| Publication Statement
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New York : Nova Science Publishers, ©2010.
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(xiv, 488 pages) : illustrations (some color)
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| ISBN
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1614701997
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: 9781614701996
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| Contents
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MACHINE LEARNING RESEARCH PROGRESS; MACHINE LEARNINGRESEARCH PROGRESS; CONTENTS; PREFACE; MACHINE LEARNING APPROACHES IN PROMOTERSEQUENCE ANALYSIS; Abstract; Introduction; Basic Elements of Promoter Structure; General Problem Definition; Promoter Prediction; Problem Definition; Datasets; Promoter Analysis; Problem Definition; Position Weight Matrix; Scanning for TFBSs; Searching for Conserved Motifs; Enhancing TFBS Discovery; Promoter Models; Conclusions; Acknowledgments; References; RECENT ADVANCES IN MACHINE LEARNINGFOR FINANCIAL MARKETS; Abstract; 1. Introduction. 1.1. Overview of Financial Market1.2. Issues and Problems; 2. Current Approaches and Solutions; 2.1. Understanding the Market; 2.1.1. Agent-Based Model (ABM); 2.1.2. Artificial Market (AM); 2.2. Current Approaches for Forecasting Market; 2.2.1. Single Data Category; 2.2.2. Mixed Data Category; 3. Review of Current Solutions; 3.1. Review of Current Solutions for Understanding Financial Market; 3.2. Review of Current Solutions for Forecasting Financial Market; 4. Research Challenges; References; A REVIEW OF BANKRUPTCY PREDICTION MODELS:THE MACHINE LEARNING PERSPECTIVE; Abstract; 1. Introduction. 2. Machine Learning Techniques2.1. Pattern Classification; 2.2. Pre-processing for Feature Selection; 2.3. Single Classifiers; 2.3.1. Neural Networks; 2.3.2. Support Vector Machines; 2.3.3. Decision Trees; 2.3.4. Genetic Algorithms; 2.3.5. Self Organizing Maps; 2.3.6. Rough Sets; 2.4. Ensemble Classifiers; 2.5. Hybrid Classifiers; 3. Comparisons of Related Work; 3.1. Types of Classifier Design; 3.2. Single Classifiers; 3.3. Hybrid Classifiers; 3.4. Baseline; 3.5. Datasets, Prediction Accuracy, and Feature Selection; 3.5.1. Datasets. 3.5.2. Datasets and the Size of Training and Testing Examples3.5.3. Feature Selection; 3.5.4. Datasets vs. Prediction Accuracy vs. Feature Selection; 4. Discussion and Conclusion; Acknowledgments; References; APPLICATION OF LEARNING MACHINESAND COMBINATORIAL ALGORITHMS IN WATERRESOURCES MANAGEMENT ANDHYDROLOGIC SCIENCES; Abstract; Introduction; Theoretical Background; Artificial Neural Networks; Support Vector Machines; Relevance Vector Machines; Locally Weighted Projection Regression; Multivariate Nonhomogeneous Hidden Markov Models; Discrete Wavelet Transformation. 1-D Wavelet Decomposition and Reconstruction2-D Wavelet Decomposition and Reconstruction; Geophysical Application of Learning Machines; Applicability of Statistical Learning Algorithms in GroundwaterQuality Modeling; Allocation of Training and Validation Datasets; Kernel Functions Selection; Combinatorial Applications; Predictive Downscaling Based on Nonhomogeneous Hidden Markov Models; Sparse Bayesian Learning Machine for Real-Time Management ofReservoir Releases; Soil Moisture Predictions using Machine Learning and ParticleSwarm Optimization.
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COMPUTERS -- Enterprise Applications -- Business Intelligence Tools.
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COMPUTERS -- Intelligence (AI) Semantics.
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Machine learning.
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| LC Classification
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Q325.5H366 2010
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| Added Entry
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Hannah Peters
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Mia Vogel
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