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

This paper provides an alternative formulation to computing the max-min inverse fuzzy relation by embedding the inherent constraints of the problem into a heuristic (objective) function. The optimization of the heuristic function guarantees maximal satisfaction of the constraints, and consequently, the condition for optimality yields solution to the inverse problem. An algorithm for computing the max-min inverse fuzzy relation is proposed. An analysis of the algorithm indicates its relatively better computational accuracy and higher speed in comparison to the existing technique for inverse computation. The principle of fuzzy abduction is extended with the proposed inverse formulation, and the better relative accuracy of the said abduction over existing works is established through illustrations with respect to a predefined error norm.

An Efficient Algorithm to Computing Max–Min Inverse Fuzzy Relation for Abductive Reasoning

The book offers an overview of a large spectrum of implementations for the computational intelligence based on neuro-fuzzy and artificial approaches. The clear and concise explanations help the reader to understand the hardware implementation aspects of the new computational intelligence paradigms.

Hardware Implementation of Intelligent Systems

This book presents a sample of the most recent research concerning the application of computational intelligence techniques and internet technology in computer games. The contents include: COMMONS GAME in intelligent environment; adaptive generation of dilemma-based interactive narratives; computational intelligence in racing games; evolutionary algorithms for board game players with domain knowledge; the ChessBrain project; electronic market games; EVE s entropy; capturing player enjoyment in computer games.

Advanced Intelligent Paradigms in Computer Games

Video tracking can be defined as an action which can estimate the trajectory of an object in the image plane as it moves within a scene. A tracker assigns consistent labels to the tracked objects in different frames of a video. The objective of this paper is to provide information on the present state of the art and to discuss future trends in the use of multi-camera tracking systems. In the literature, three main types of multi-camera tracking system have been outlined. The first type relies on challenges in the camera tracking system. The second concerns the methodology of tracking systems in general. The third type relies on current trends in camera tracking systems. We provide an overview of the current research status by summarizing promising avenues for further research.

Lakhmi C. Jain

Papers

An Efficient Algorithm to Computing Max–Min Inverse Fuzzy Relation for Abductive Reasoning

Hardware Implementation of Intelligent Systems

Advanced Intelligent Paradigms in Computer Games

Human tracking: a state-of-art survey

Intelligence-Based Systems Engineering