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Advances in Materials Science Research. Volume 33
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Advances in Machine Learning Research
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Editors: Sharad Shandilya (Virginia, US)
Book Description:
In the digital age, the field of machine learning has lived up to its promise of learning from and leveraging data in diverse fields, creating knowledge and driving decisions. This book intends to detail advances in the state-of-the-art in machine learning, one of the fastest emerging fields in the industry and one of the most popular fields of research in computational sciences. The roots of machine learning methods can be traced back to both statistics and computer science. Its story continues to evolve and the future is set to be greatly influenced through ML’s contributions to the human knowledge-base as well as the economic engine.
Applied machine learning research enthuses the masses with applications such as video games that interact through a camera, self-driving cars, etc. At the same time, more basic machine learning research holds the potential to impact knowledge elicitation, learning, predictions, decisions, and optimizations in fields ranging from environmental/biomedical/clinical informatics on one hand to online retail and search on the other. Accordingly, the contents of this volume are geared to present a full-color palette consisting of improved optimization algorithms, novel ANN design architectures, along with customized methods for mining an environmental dataset, pattern recognition in images, and for improved document and text search. While many out-of-the-box implementations of machine learning algorithms are currently available, customized methods developed by honed and innovative researchers continue to provide significant improvements in various contexts. Advancements through basic research continue to break the barriers of the extent of ML’s contribution to the world. (Imprint: Novinka)

Table of Contents:
Preface pp.vii-x

Chapter 1. Enhancing Document Search with a Dynamic Artificial Neural Network
(M. Ghiassi, M. Olschimke, Santa Clara University, Santa Clara, CA, US, Dörffler + Partner GmbH, Mühlhausen, Germany)pp.1-34

Chapter 2. Combination of Depth and Texture Descriptors for Gesture Recognition
(Loris Nanni, Alessandra Lumini, Fabio Dominio, Mauro Donadeo and Pietro Zanuttigh, Department of Information Engineering,
University of Padova, Padova, and DISI, University of Bologna, Cesena, Italy)pp.35-64

Chapter 3. Optimization for Multi-Layer Perceptron: Without the Gradient
(Bojan Ploj)pp.65-112

Chapter 4. Prediction of Cyanotoxin Production along with Cyanobacteria Presence Using Genetic Algorithms and Multivariate Adaptive Regression Splines
(J.R. Alonso Fernández, C. Díaz Muñiz, P.J. García Nieto, F. Sánchez Lasheras and F.J. de Cos Juez, Cantabrian Basin Authority, Ministry of Agriculture, Food and Environment, and University of Oviedo, Spain)
pp.113-134
Index pp.137-143

   Series:
      Engineering Tools, Techniques and Tables
   Binding: Softcover
   Pub. Date: 2014 - 3rd Quarter
   Pages: 153. 6x9 - (NBC-C)
   ISBN: 978-1-63321-209-1
   Status: AV
  
Status Code Description
AN Announcing
FM Formatting
PP Page Proofs
FP Final Production
EP Editorial Production
PR At Prepress
AP At Press
AV Available
  
Special Focus Titles
01.Peter Singer’s Ethics: A Critical Appraisal
02.Sexism: Past, Present and Future Perspectives
03.Body and Politics: Elite Disability Sport in China
04.Childhood and Adolescence: Tribute to Emanuel Chigier, 1928-2017
05.Renal Replacement Therapy: Controversies and Future Trends
06.Food-Drug Interactions: Pharmacokinetics, Prevention and Potential Side Effects
07.Terrorism and Violence in Islamic History and Theological Responses to the Arguments of Terrorists
08.International Event Management: Bridging the Gap between Theory and Practice
09.The Sino-Indian Border War and the Foreign Policies of China and India (1950-1965)
10.Tsunamis: Detection, Risk Assessment and Crisis Management
11.Sediment Watch: Monitoring, Ecological Risk Assessment and Environmental Management
12.Self-Regulated Learners: Strategies, Performance, and Individual Differences

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Advances in Machine Learning Research