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

Cognitive Engineering: A Distributed Approach to Machine Intelligence (Advanced Information and Knowledge Processing)

Fuzzy control is an effective method widely employed in the complex control field. However, in the fuzzy control, the adjustment (tuning) of the shape of membership functions and the acquisition of fuzzy rules is still a very active research field. To solve the tuning problem, a number of fusion/union methods using neural networks (NN) or genetic algorithms (GA) have been proposed. By scanning through these methods, we find that these methods are roughly divided into some categories depending on the grade of fusion/union between fuzzy models and NN, GA. In this papers, the fusion/union models are discussed and their applications in the field of control are explained.

Acquisition of fuzzy knowledge by NN and GA-A survey of the fusion and union methods proposed in Japan

Medical Bioinformatics: Detecting Molecular Diseases with Case-Based Reasoning.- Prototype Based Recognition of Splice Sites.- Contact Based Image Compression in Biomedical High-Throughput Screening Using Artificial Neural Networks.- Discriminative Clustering of Yeast Stress Response.- A Dynamic Model of Gene Regulatory Networks Based on Inertia Principle.- Class Prediction with Microarray Datasets.- Random Voronoi Ensembles for Gene Selection in DNA Microarray Data.- Cancer Classification with Microarray Data Using Support Vector Machines.- Artificial Neural Networks for Reducing the Dimensionality of Gene Expression Data.

Bioinformatics using computational intelligence paradigms

This monograph provides a comprehensive research review of intelligent techniques for personalisation of e-learning systems. Special emphasis is given to intelligent tutoring systems as a particular class of e-learning systems, which support and improve the learning and teaching of domain-specific knowledge. A new approach to perform effective personalization based on Semantic web technologies achieved in a tutoring system is presented. This approach incorporates a recommender system based on collaborative tagging techniques that adapts to the interests and level of students' knowledge. These innovations are important contributions of this monograph. Theoretical models and techniques are illustrated on a real personalised tutoring system for teaching Java programming language. The monograph is directed to, students and researchers interested in the e-learning and personalization techniques.

E-Learning Systems

The Psychological Basis of Cognitive Modeling.- Parallel and Distributed Logic Programming.- Distributed Reasoning by Fuzzy Petri Nets: A Review.- Belief Propagation and Belief Revision Models in Fuzzy Petri Nets.- Building Expert Systems Using Fuzzy Petri Nets.- Distributed Learning Using Fuzzy Cognitive Maps.- Unsupervised Learning by Fuzzy Petri Nets.- Supervised Learning by a Fuzzy Petri Net.- Distributed Modeling of Abduction, Reciprocity, and Duality by Fuzzy Petri Nets.- Human Mood Detection and Control: A Cybernetic Approach.- Distributed Planning and Multi-agent Coordination of Robots.

Lakhmi C. Jain

Papers

Cognitive Engineering: A Distributed Approach to Machine Intelligence (Advanced Information and Knowledge Processing)

Acquisition of fuzzy knowledge by NN and GA-A survey of the fusion and union methods proposed in Japan

Bioinformatics using computational intelligence paradigms

E-Learning Systems

Cognitive Engineering: A Distributed Approach to Machine Intelligence