eBook By Category (Machine Learning)

`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 systems in a dynamic and changing environment, machine learning methods have been playing an increasingly important role in many software development and maintenance tasks

Advances in Machine Learning Applications in Software Engineering provides analysis, characterization, and refinement of software engineering data in terms of machine learning methods

This is the first 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 of machine learning, an exciting and relevant, both from the practical and theoretical points of view, area in modern computer science and information technology

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 can be applied to a vast set of applications such as web and text mining, marketing, medical science, bioinformatics and business intelligence

This volume contains the revised versions of selected papers in the field of data analysis, machine learning and applications presented during the 31st Annual Conference of the German Classification Society (Gesellschaft für Klassifikation - GfKl)

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 past decade, and this book provides a long-needed systematic and unified treatment of theoretical and practical aspects of GPs in machine learning

The treatment is comprehensive and self-contained, targeted at researchers and students in machine learning and applied statistics

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 prestigious researchers in this field

This book covers the three main learning systems; symbolic learning, neural networks and genetic algorithms as well as providing a tutorial on learning casual influences

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 systems that analyze past sales data to predict customer behavior, recognize faces or spoken speech, optimize robot behavior so that a task can be completed using minimum resources, and extract knowledge from bioinformatics data

Introduction to Machine Learning is a comprehensive textbook on the subject, covering a broad array of topics not usually included in introductory machine learning texts

Handling inherent uncertainty and exploiting compositional structure are fundamental to understanding and designing large-scale systems

Statistical relational learning builds on ideas from probability theory and statistics to address uncertainty while incorporating tools from logic, databases, and programming languages to represent structure

In Introduction to Statistical Relational Learning, leading researchers in this emerging area of machine learning describe current formalisms, models, and algorithms that enable effective and robust reasoning about richly structured systems and data

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 elegant learning machines that use a central concept of SVMs?-kernels--for a number of learning tasks

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 quarter-century but a new phase

As can be seen in these pages, machine learning emerged to declare itself as a seed-bed of new theory, as a practical tool in engineering disciplines, and as material for new mental models in human sciences

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 computational learning theory

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 level undergraduate and introductory level graduate courses in machine learning



User review
Good as an Introduction/ to get overview on ML
This is extremely intuitive and general point of view on ML

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 topics such as intelligent object detection, foveated vision systems, online learning paradigms, reinforcement learning for a mobile robot, object tracking and motion estimation, 3D model construction, computer vision system and user modelling using dialogue strategies

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

Focusing on complex media and how to convert raw data into useful information, this book offers both introductory and advanced material in the combined fields of machine learning and image/video processing

The machine learning techniques presented enable readers to address many real world problems involving complex data

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 programs, exhibit richer structures than simple, isolated data items

A number of pixels in a digital image collectively conveys certain visual content to viewers

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 and state-of-the-art reviews by leading researchers all over the world including pointers to challenges and opportunities for future research directions

The main goals of the book are identification of good practices for the use of learning strategies in DAR, identification of DAR tasks more appropriate for these techniques, and highlighting new learning algorithms that may be successfully applied to DAR

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 potential for machine learning algorithms in solving significant commercial and industrial problems, widening the foundation for exploitation of these and related algorithms

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 nature of machine learning algorithms as either `local learning`or `global learning

`This theory not only connects previous machine learning methods, or serves as roadmap in various models, but ?

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 which no label data are given)

Interest in SSL has increased in recent years, particularly because of application domains in which unlabeled data are plentiful, such as images, text, and bioinformatics

This first comprehensive overview of SSL presents state-of-the-art algorithms, a taxonomy of the field, selected applications, benchmark experiments, and perspectives on ongoing and future research