Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

Machine learning

Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Pattern Recognition and Machine Learning

Data Mining Practical Machine Learning Tools and Techniques

How has Japan become a major economic power, a world leader in the automotive and electronics industries? What is the secret of their success? The consensus has been that, though the Japanese are not particularly innovative, they are exceptionally skilful at imitation, at improving products that already exist. But now two leading Japanese business experts, Ikujiro Nonaka and Hiro Takeuchi, turn this conventional wisdom on its head: Japanese firms are successful, they contend, precisely because they are innovative, because they create new knowledge and use it to produce successful products and technologies. Examining case studies drawn from such firms as Honda, Canon, Matsushita, NEC, 3M, GE, and the U.S. Marines, this book reveals how Japanese companies translate tacit to explicit knowledge and use it to produce new processes, products, and services.

The knowledge-creating company : how Japanese companies create the dynamics of innovation

How to Do Things With Words

Advances in Intelligent Information Hiding and Multimedia Signal Processing

This book constitutes the refereed proceedings of the First International Symposium on Agent and Multi-Agent Systems - Technologies and Applications, KES-AMSTA 2007, held in Wroclaw, Poland in May/June 2007. China in November 2006. The 110 revised papers presented were carefully reviewed and selected from 464 submissions and contain 4 papers from the doctoral track, and 38 papers from 3 special sessions. The papers are organized in topical sections representing the following conference tracks: methodological aspects of agent systems, agent-oriented Web applications, mobility aspects of agent systems and ontology management, multi-agent resource allocation, negotiating agents, agents for network management, agent approaches to robotic systems, market agents and other applications; the doctoral track; special sessions on mobile agent application and its development, agent on networked media and its applications on next-generation convergence network, as well as on intelligent and secure agent for digital content management.

Agent and Multi-Agent Systems: Technologies and Applications First KES International Symposium, KES-AMSTA 2007, Wroclaw, Poland, May 31-June 1, 2007, ... / Lecture Notes in Artificial Intelligence)

Normally a decision support system is build to solve problem where multi-criteria decisions are involved. The database is the vital part of the decision support containing the information or data that is used in decision-making process. This is the field where engineers and scientists try to apply several heuristics and soft computing techniques such as learning, search and modelling the imprecise information to obtain optimal decisions. In this paper, we present the proposed a generic framework using evolutionary fuzzy systems to obtain decision rules automatically and the usage of fuzzy inference system to process the imprecise information. Some simulation results demonstrating the difficulties to decide the optimal quantity of membership functions, shape and parameters are also provided.

Adaptive Database Learning in Decision Support Systems Using Evolutionary Fuzzy Systems: A Generic Framework

The book at hand constitutes the inaugural volume in the new Springer series on Learning and Analytics in Intelligent Systems. The series aims at making available a publication of books in hardcopy and soft-copy form on all aspects of learning, analytics and advanced intelligent systems and related technologies. These disciplines are strongly related and complement one another significantly. Thus, the new series encourages a unified/integrated approach to themes and topics in these disciplines which will result in significant cross-fertilization, research advancement and new knowledge creation. To maximize dissemination of research results and knowledge, the series will publish edited books, monographs, handbooks, textbooks and conference proceedings. The book at hand is directed towards professors, researchers, scientists, engineers and students. An extensive list of references at the end of each chapter guides readers to probe further into application areas of interest to them.

Lakhmi C. Jain

Papers

Advances in Intelligent Information Hiding and Multimedia Signal Processing

Agent and Multi-Agent Systems: Technologies and Applications First KES International Symposium, KES-AMSTA 2007, Wroclaw, Poland, May 31-June 1, 2007, ... / Lecture Notes in Artificial Intelligence)

Adaptive Database Learning in Decision Support Systems Using Evolutionary Fuzzy Systems: A Generic Framework

Applications of Learning and Analytics in Intelligent Systems

A future framework for interfacing BDI agents in a real-time teaming environment