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

The paper presents an Adaptive Intelligent Learning System (AILS) designed to be used with any Learning Management System (LMS). The adaptiveness provides uniquely identifying and monitoring the learner's learning process according to the learner's profile. AILS has been implemented as a multi-agent system. The agents were developed as JADE agents. The paper presents the learning model, system components, agent behavior in learner scenarios, the ontologies used in agent communications, and the adaptive strategies. The sample application of the AILS to a dummy LMS is also given.

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Intelligent learning system for online learning

This chapter presents introductory remarks on computational intelligence in healthcare practice, and it provides a brief outline for each of the succeeding chapters in the remainder of this book.

Introduction to Computational Intelligence in Healthcare

The philosophical roots of the Belief-Desire-Intention model lie in Bratman's formulation of an intention theory of planning, in which he sought to make sense of the notion of future-directed intention. Implementations of BDI mainly follow the original Procedural Reasoning System model. BDI has a sound logical basis, exemplified by the Logic Of Rational Agents. While the LORA formulation has a temporal logic component, however, this does not translate into any ability for the agent to reason about actual time. Being able to reason about actual time would bring significant benefits for BDI agents, such as the ability for agents to communicate deadlines and to plan and schedule activities in a cooperating group. Given a suitable representation of temporal knowledge, an agent could learn about the temporal aspects of its own actions and processes, and this knowledge could be used as input to the planning process. This paper outlines a possible implementation strategy for the representation of, and the capacity to reason about, actual time.

Reasoning about time in a BDI architecture

This paper describes a vision system for extracting and recognising partially occluded 2D visual landmarks. The system is developed based on the traditional template matching approach and a memory feedback modulation (MFM) mechanism. It identifies the obscured portions and selectively enhances non-occluded areas of the landmark, while simultaneously suppressing background clutters of the bottom-up edge processed input images. The architecture has been tested with a large number of real images with varying levels of landmark concealment and further evaluated using a vision-based navigating robot in the laboratory environment.

A vision system for partially occluded landmark recognition

This chapter introduces a number of intelligent methodologies and techniques stemmed from Artificial Intelligence (AI). An overview of various intelligent models arisen from expert systems, artificial neural networks, fuzzy logic, genetic algorithms, decision trees, and agent technologies is presented. Application examples of these intelligent models in various domains are also presented. Then, the contribution of each chapter included in this book is described. A summary of concluding remarks is presented at the end of the chapter.

Lakhmi C. Jain

Papers

Intelligent learning system for online learning

Introduction to Computational Intelligence in Healthcare

Reasoning about time in a BDI architecture

A vision system for partially occluded landmark recognition

Advances in intelligent methodologies and techniques