Machine Learning: Concepts, Methodologies, Tools And Applications, 1st Edition

  • Published By:
  • ISBN-10: 1609608194
  • ISBN-13: 9781609608194
  • DDC: 006.3
  • Grade Level Range: College Freshman - College Senior
  • 2141 Pages | eBook
  • Original Copyright 2011 | Published/Released November 2011
  • This publication's content originally published in print form: 2011

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This reference offers a wide-ranging selection of key research in a complex field of study,discussing topics ranging from using machine learning to improve the effectiveness of agents and multi-agent systems to developing machine learning software for high frequency trading in financial markets.

Table of Contents

Front Cover.
Title Page.
Copyright Page.
Editorial Board.
List of Contributors.
1: Fundamental Concepts and Theories.
2: A Comparison of Human and Computer Information Processing.
3: Machine Learning.
4: Machine Learning through Data Mining.
5: Calibration of Machine Learning Models.
6: Classification of Web Pages Using Machine Learning Techniques.
7: 3D Modelling and Artificial Intelligence: A Descriptive Overview.
8: An Overview of Knowledge Translation.
9: Adaptive Technology and Its Applications.
10: Adaptive Algorithms for Intelligent Geometric Computing.
11: Different Roles and Definitions of Spatial Data Fusion.
12: Development and Design Methodologies.
13: Machine Learning as a Commonsense Reasoning Process.
14: Motivated Learning for Computational Intelligence.
15: Designing a Computational Model of Learning.
16: Intelligent MAS in System Engineering and Robotics.
17: Information Hiding by Machine Learning: A Method of Key Generation for Information Extracting Using Neural Network.
18: Rule Engines and Agent-Based Systems.
19: Higher Order Neural Network Architectures for Agent-Based Computational Economics and Finance.
20: A Bayesian Based Machine Learning Application to Task Analysis.
21: Combining Classifiers and Learning Mixture-of-Experts.
22: Designing Unsupervised Hierarchical Fuzzy Logic Systems.
23: A Self-Organizing Neural Network to Approach Novelty Detection.
24: Empirical Inference of Numerical Information into Causal Strategy Models by Means of Artificial Intelligence.
25: Designing Light Weight Intrusion Detection Systems: Non-Negative Matrix Factorization Approach.
26: A Multi-Agent Machine Learning Framework for Intelligent Energy Demand Management.
27: Modelling Gene Regulatory Networks Using Computational Intelligence Techniques.
28: Tools and Technologies.
29: Application of Machine Learning Techniques to Predict Software Reliability.
30: Application of Artificial Immune Systems Paradigm for Developing Software Fault Prediction Models.
31: A Recovery-Oriented Approach for Software Fault Diagnosis in Complex Critical Systems.
32: Artificial Intelligence Techniques for Unbalanced Datasets in Real World Classification Tasks.
33: Hybrid Meta-Heuristics Based System for Dynamic Scheduling.
34: Differential Learning Expert System in Data Management.
35: Hybrid Intelligent Diagnosis Approach Based on Neural Pattern Recognition and Fuzzy Decision-Making.
36: Machine Learning Approach to Search Query Classification.
37: Machine Learning in Morphological Segmentation.
38: Machine Learning Techniques for Network Intrusion Detection.
39: A Machine Learning Based Meta-Scheduler for Multi-Core Processors.
40: Automatic Semantic Annotation Using Machine Learning.
41: Electricity Load Forecasting Using Machine Learning Techniques.
42: Non-Topical Classification of Query Logs Using Background Knowledge.
43: Prediction of Compound-Protein Interactions with Machine Learning Methods.
44: Secure Key Generation for Static Visual Watermarking by Machine Learning in Intelligent Systems and Services.
45: Adaptive Ensemble Multi-Agent Based Intrusion Detection Model.
46: Class Prediction in Test Sets with Shifted Distributions.
47: Bankruptcy Prediction by Supervised Machine Learning Techniques: A Comparative Study.
48: Bankruptcy Prediction through Artificial Intelligence.
49: Utilization and Application.
50: Machine Learning and Data Mining in Bioinformatics.
51: Machine Learning for Biometrics.
52: Pattern Discovery from Biological Data.
53: Computer-Aided Detection and Diagnosis of Breast Cancer Using Machine Learning, Texture and Shape Features.