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

By their very nature, intelligent agents possess four important social abilities. These include the ability to communicate, cooperate, collaborate and the need to be coordinated. This paper presents an overview of two of these social abilities, that of being coordination and cooperation. The discussion develops the theory of each and derives the current definitions. The definitions will then be linked into a single multi-agent system (MAS) model, Agent Coordination and Cooperation Cycle Model. This shows a cognitive loop that replicates the link between coordination and cooperation in systems such as organizations, management and biological systems. This paper will also present the advantages, consequences and challenges associated with the implementation of Agent Coordination and Cooperation Cognitive Model (AC3M) within intelligent multi-agent systems.

An overview of agent coordination and cooperation

A necessary strategy to improve our technologies is to provide them with useful pieces of deterministic previous knowledge about the processes and the equipment. Our attention was previously focused on the industrial control systems, implemented with low level devices (controllers, sensors, actuators), that need knowledge on the specific controlled plants as well as on the general theoretical foundations. This was done on-line with the help of internal models, or off-line with planners whose design is assisted by simulations. This paper is orienting the same effort in the direction of the intelligent sensors field. A brief overview of the relationship between sensors and knowledge is provided. A particular architecture of sensors relying on modeling techniques and some internal model estimators for the velocity and for the weariness of the railway cars are illustrating the role of the models in sensors and estimators.

World knowledge for sensors and estimators by models and internal models

The recombination operator plays a very important role in genetic algorithms. We present binary coded genetic algorithms in which more than two parents are involved in the recombination operation. We propose two types of multi-parent recombination operators, the multi-cut (MX) and seed crossover (SX). Each of these operators is a natural generalization of two parents recombination operator. These operators are evaluated on the De Jong standard test functions. The results showed clearly that the multi-parent recombinations lead to better performance, although the performance improvement for different techniques were found to be dependent on problems.

http://www2.hannan-u.ac.jp/~tsutsui/ps/kes98-mp.pdf

On the effect of multi-parents recombination in binary coded genetic algorithms

Machine Learning Paradigms - Advances in Learning Analytics.

W atermarking is one useful solution for digital rights management (DRM) systems , and it is a popular research topic in the last decade . In this paper, besides the inherent behavior to conquer against intentional or unintentional attacks for watermarking, we not only watch the survivability of embedded watermark, but also focus on retaining the watermarked image quality with the aid of multiple description coding (MDC). MDC is a technique for error resilient coding, suitable for transmitting compressed data over multiple channels. In this paper, we propose a n ew algorithm on vector quantization (VQ) based image watermarking, which is suitable for error-resilient transmission. By incorporating watermarking with MDC , the scheme we proposed for embedding three watermarks can effectively overcome channel impairments while retaining the capability for ownership protection. With the promising simulation results presented, we can demonstrate the utility and practicability of our algorithm.

Lakhmi C. Jain

Papers

An overview of agent coordination and cooperation

World knowledge for sensors and estimators by models and internal models

On the effect of multi-parents recombination in binary coded genetic algorithms

Machine Learning Paradigms - Advances in Learning Analytics.

Error-Resilient Triple-Watermarking with Multiple Description Coding