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

We investigate the problem of agent communication when such agents are cooperating rather than competing. We do this using the statistical technique of canonical correlation analysis. We consider two stochastic process methods for performing canonical correlation analysis (CCA). The first creates a Gaussian Process formulation of regression in which we use the current projection of one data set as the target for the other and then repeat in the opposite direction; this is useful in the very limited environment in which the agents have prior and precise knowledge of the nature of their interaction. We extend the problem by creating a problem which no single agent can solve, but for which together they can find a solution. We investigate methods by which we can automatically find the number and composition of the groups necessary to solve the problem. Finally we develop a Dirichlet process of Gaussian models in which the Gaussian models are determined by Probabilistic CCA [2]. The Dirichlet Process enables us to have groups of agents cooperating on a task without having to specify in advance how many groups of agents there are or how many agents join each group.

A stochastic process formulation of learning in cooperative agents

In Lin's method [1], images are divided into non-overlapping three-pixel blocks. Each pixel block contains two pairs composed of two neighboring pixels. Absolute differences between pairs are calculated. And meanwhile, the absolute difference having the highest occurrence rate is obtained. 1-bit watermark is embedded into a pixel pair whose absolute difference is equal to this obtained value. Since each pixel block contains two differences, Lin's method achieves embedding rate of at most 2/3 bpp (bit per pixel) for a single embedding process. With the aim of further increasing embedding rate, we modify the embedding process in Lin's method to keep the third pixel of a pixel block unaltered in the proposed method. Then, this unchanged pixel is used again to reform into a new pixel block with its two neighboring pixels. As a result, the embedding rate can reach to 1 bpp for a single embedding process.

Reversible watermarking based on invariant relation of three pixels

The chapter presents a brief description of chapters that contribute to the recent advances in numerical methods in continuum mechanics, computational physics Also, this chapter deals with machine learning and computer science The fourth part of the book presents novel computational methods in continuum mechanics Modern methods in mathematical physics are presented in the fifth part of the book The sixth part of the book deals with machine learning Computer science is discussed in the seventh part of the book

Recent Advances in Numerical Methods, Machine Learning, and Computer Science

1 Department of Statistics and Applied Probability, University of California Santa Barbara, Santa Barbara, CA 93106-3110, USA holmes@pstat.ucsb.edu 2 Defence Science and Technology Organisation, PO. Box. 1500, Edinburgh South Australia SA 5111, Australia jeffrey.tweedale@dsto.defence.gov.au 3 School of Electrical and Information Engineering, University of South Australia, Adelaide, Mawson Lakes Campus, South Australia SA 5095, Australia lakhmi.jain@unisa.edu.au

http://link.springer.com/content/pdf/10.1007/978-3-642-23151-3_1.pdf

Chapter 1 Advances in Intelligent Data Mining

We address four problems related to the precision in analog hardware implementations of fuzzy and neuro-fuzzy systems, namely the precision of approximation/ interpolation; errors due to hardware implementation — manufacturing factors in integrated circuit (IC); errors due to sensitivity to external factors, and dynamical errors.

Lakhmi C. Jain

Papers

A stochastic process formulation of learning in cooperative agents

Reversible watermarking based on invariant relation of three pixels

Recent Advances in Numerical Methods, Machine Learning, and Computer Science

Chapter 1 Advances in Intelligent Data Mining

Errors in fuzzy hardware for control and decision systems