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

Colour image classification plays an important role in computer vision and pattern recognition. Traditional classification research mainly focuses on developing novel techniques that are efficient for image representation or classification. By processing considerable visual information, human can handle the complicated classification tasks quite effectively. Inspirited by the structure of the visual cortex, we propose a multi-agent colour image classification architecture (MACICA). Agents within a multi-agent system (MAS) architecture are programmed to deliver specific image classification capabilities. The MACICA provides an efficient classification output by sharing knowledge, communication and team work. The architecture is flexible and dynamic, while the platform has produced encouraging results, which are presented in the paper.

A multi-agent system architecture to classify colour images

In this chapter a brief description of syntax, semantics, topology, ontology and taxonomy is discussed in relation to agency theory. The Open Systems Interconnection (OSI) model is included to help aid the ready align the technical, physical and cognitive connections. At present the communications layer is being embedded into many agent architectures, when in fact, it should remain as an underlying transport mechanism. However the interoperable functions are still required as are a standard methodology of exchanging information and intent. These and related subjects mechanisms are also included to generate further discussion into what the future standard should include.

Agent Interoperability and Adaptation

Innovations in Defence Support Systems – 1

The information footprints of a rapidly increasing influx of Internet users present us with an immense source of information that ultimately contributes to the construction of innovative web technology suitable for the future generations. Likewise, Web Intelligence has been presented as the usage of advanced techniques in Artificial Intelligence and Information Technology for the purpose of exploring, analysing, and extracting knowledge from Web data. In this chapter, the use of Web Intelligence is discussed together with ways in which a wide range of research is benefiting this area for the long-term. Also the books’ purpose and structure are introduced, together with a list of resources to explore this area of research further.

Innovations in Web Intelligence

In this chapter we propose a solution to the Electroencephalography (EEG) inverse problem combining two techniques, which are the Sequential Monte Carlo (SMC) method for estimating the coordinates of the first two non-correlated dominative brain zones (represented by their respective current dipoles) and spatial filtering which is done by beamforming based on EEG data. Beamforming (BF) gives estimates of the respective source moments. In order to validate this novel approach for brain source localization, EEG data from dipoles with known locations and known moments are generated and artificially corrupted with noise. The noise represents the overall influence of other brain sources but they are not brain disturbances. When the power of the EEG signal due to the main brain sources is higher than the summed effect of all other secondary sources, the estimation of the localization of the leading sources is reliable and repetitive over a number of Monte Carlo runs.

Lakhmi C. Jain

Papers

A multi-agent system architecture to classify colour images

Agent Interoperability and Adaptation

Innovations in Defence Support Systems – 1

Innovations in Web Intelligence

A Sequential Monte Carlo Approach for Brain Source Localization