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Machine Learning, Neural and Statistical Classification (Ellis Horwood Series in Artificial Intelligence)
Statistical, machine learning and neural network approaches to classification are all covered in this volume. Contributions have been integrated to provide an objective assessment of the...
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond (Adaptive Computation and Machine Learning)
In the 1990s, a new type of learning algorithm was developed, based on results from statistical learning theory: the Support Vector Machine (SVM). This gave rise to a new class of theoretically...
Machine Learning and Robot Perception (Studies in Computational Intelligence) (Studies in Computational Intelligence)
This book presents some of the most recent research results in the area of machine learning and robot perception. The chapters represent new ways of solving real-world problems. The book covers...
Innovations in Machine Learning: Theory and Applications (Studies in Fuzziness and Soft Computing)
Machine learning is currently one of the most rapidly growing areas of research in computer science. In compiling this volume we have brought together contributions from some of the most...
Advances in Machine Learning Applications in Software Engineering
`Machine learning is the study of building computer programs that improve their performance through experience. To meet the challenge of developing and maintaining larger and complex software...
Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning)
Gaussian processes (GPs) provide a principled, practical, probabilistic approach to learning in kernel machines. GPs have received increased attention in the machine-learning community over the...
Machine Learning for Multimedia Content Analysis (Multimedia Systems and Applications)
Challenges in complexity and variability of multimedia data have led to revolutions in machine learning techniques. Multimedia data, such as digital images, audio streams and motion video...
Semi-Supervised Learning (Adaptive Computation and Machine Learning)
In the field of machine learning, semi-supervised learning (SSL) occupies the middle ground, between supervised learning (in which all training examples are labeled) and unsupervised learning (in...
Machine Learning in Document Analysis and Recognition (Studies in Computational Intelligence)
The objective of Document Analysis and Recognition (DAR) is to recognize the text and graphical components of a document and to extract information. This book is a collection of research papers...
Machine Learning for Audio, Image and Video Analysis: Theory and Applications (Advanced Information and Knowledge Processing)
Machine Learning involves several scientific domains including mathematics, computer science, statistics and biology, and is an approach that enables computers to automatically learn from data....
Data Analysis, Machine Learning and Applications: Proceedings of the 31st Annual Conference of the Gesellschaft für Klassifikation e.V., Albert-Ludwigs-Universität ... Data Analysis, and Knowledge Organization)
Data analysis and machine learning are research areas at the intersection of computer science, artificial intelligence, mathematics and statistics. They cover general methods and techniques that...
Introduction to Machine Learning (Adaptive Computation and Machine Learning)
The goal of machine learning is to program computers to use example data or past experience to solve a given problem. Many successful applications of machine learning exist already, including...
Machine Intelligence 14: Applied Machine Intelligence
This 14th volume of the classic series on machine intelligence contains papers on complex decision taking, inductive logic programming, applied machine learning, dynamic control, and...
Machine Intelligence 13: Machine Intelligence and Inductive Learning (Machine Intelligence)
The present volume records the Machine Intelligence Workshop of 1992, held at Strathclyde University's Ross Priory retreat on Loch Lomond, Scotland. Here the series entered not only its second...
Machine Learning: Modeling Data Locally and Globally (Advanced Topics in Science and Technology in China)
Machine Learning - Modeling Data Locally and Globally presents a novel and unified theory that tries to seamlessly integrate different algorithms. Specifically, the book distinguishes the inner...
Introduction to Statistical Relational Learning (Adaptive Computation and Machine Learning)
Handling inherent uncertainty and exploiting compositional structure are fundamental to understanding and designing large-scale systems. Statistical relational learning builds on ideas from...
Machine Learning (Mcgraw-Hill International Edit)
This book covers the field of machine learning, which is the study of algorithms that allow computer programs to automatically improve through experience. The book is intended to support upper...
Advances in Machine Learning I: Dedicated to the Memory of Professor Ryszard S. Michalski (Studies in Computational Intelligence)
This is thefirst volume of a large two-volume editorial project we wish to dedicate to the memory of the late Professor Ryszard S. Michalski who passed away in 2007. He was one of the fathers...
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