Efficiency and Scalability Methods for Computational Intellect, 1st Edition

  • Boris Igelnik
  • Published By:
  • ISBN-10: 1466639431
  • ISBN-13: 9781466639430
  • DDC: 006.3
  • Grade Level Range: College Freshman - College Senior
  • 327 Pages | eBook
  • Original Copyright 2013 | Published/Released July 2013
  • This publication's content originally published in print form: 2013

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Efficiency and Scalability Methods for Computational Intellect presents various theories and methods for approaching the problem of modeling and simulating intellect in order to target computation efficiency and scalability of proposed methods. Researchers, instructors, and graduate students will benefit from this current research and will in turn be able to apply the knowledge in an effective manner to gain an understanding of how to improve this field.

Table of Contents

Front Cover.
Title Page.
Copyright Page.
Editorial Advisory Board.
List of Reviewers.
Table of Contents.
Detailed Table of Contents.
1: Efficient and Scalable Methods in Machine Learning, Data Mining, and Medicine.
2: Up-to-Date Feature Selection Methods for Scalable and Efficient Machine Learning.
3: Online Machine Learning.
4: Uncertainty in Concept Hierarchies for Generalization in Data Mining.
5: Efficiency and Scalability Methods in Cancer Detection Problems.
6: Efficient and Scalable Methods in Image Processing, Robotics, Control, Computer Networks Defense, Human Identification, and Combinatorial Optimization.
7: The Kolmogorov Spline Network for Authentication Data Embedding in Images.
8: Real-Time Fuzzy Logic-Based Hybrid Robot Path-Planning Strategies for a Dynamic Environment.
9: Evolutionary Optimization of Artificial Neural Networks for Prosthetic Knee Control.
10: Techniques to Model and Derive a Cyber–Attacker's Intelligence.
11: A Scalable Approach to Network Traffic Classification for Computer Network Defense using Parallel Neural Network Classifier Architectures.
12: Biogeography–Based Optimization for Large Scale Combinatorial Problems.
13: Concepts.
14: Kolmogorov Superpositions: A New Computational Algorithm.
15: Evaluating Scalability of Neural Configurations in Combined Classifier and Attention Models.
16: Numerical Version of the Non–Uniform Method for Finding Point Estimates of Uncertain Scaling Constants.
17: Widely Linear Estimation with Geometric Algebra.
Compilation of References.
About the Contributors.