Advances in machine learning research
Author(s)
Bibliographic Information
Advances in machine learning research
（Engineering tools, techniques and tables series）
Nova Science Publishers, [c2014]
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Note
Includes bibliographical references and index
Description and Table of Contents
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.
Table of Contents
PrefaceChapter 1. Enhancing Document Search with a Dynamic Artificial Neural Network(M. Ghiassi, Santa Clara University, Santa Clara, CA, US)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)Chapter 3. Optimization for Multi-Layer Perceptron: Without the Gradient(Bojan Ploj)For Complete Table of Contents, please visit our website athttps://www.novapublishers.com/catalog/product_info.php?products_id=50219
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