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 reports the development of a temporal neuro-fuzzy model using fuzzy reasoning which is capable of representing the temporal information. The system is implemented as a feedforward multilayer neural network. The learning algorithm is a modification of the backpropagation algorithm. The system is aimed to be used in a medical diagnosis systems.

A temporal neurofuzzy model for rule-based systems

This chapter presents some recent advances in the area of computer vision. The various stages involved in the area of image processing and their interpretation are described. The first step is that of image registration. That is, the overlay two or more images of the same scene. These are taken from different viewpoints, at different times or possibly by different sensors. The next phase is image preprocessing. This mainly involves image enhancement and clearing for example. Other problem is that of image analysis. This is the extraction of important features of the image. Having obtained a description of the image, the process of object (pattern) recognition can be performed. All of these tasks are very important and useful, they still do not give a semantic interpretation of images. Image interpretation, similar image searching is still a major challenge facing researchers. The second part of this chapter summarises the remaining chapters of the book.

Advances in Intelligent Image Analysis

A Conceptual Model for Holonic Manufacturing Execution

In this paper we present our experience in using a new technology for teaching engineering students enrolled in the 2nd year course Signals and Systems in the School of Electrical and Information Engineering at the University of South Australia. A set of Interactive Electronic Tutorials (lET) was created with the intention to completely replace the conventional tutorials. On students' request we run both electronic and conventional tutorials in parallel and in our previous study we showed students' strong preference for conventional tutorials over the lET. As we witness the technology developing in the direction where electronic environment is becoming a part of all aspects of everyday life, there is no way back to the 'black board'. Rather, we take the opportunity to analyse the outcomes of our unintentional experiment to learn how the new technology affects the learning and in particular how it can be used for the full benefit of students. In this paper we perform analysis of the lET from different aspects and discuss what can be done to make the lET more attractive for students.

Electronic Tutorials - Interactive and Attractive

This paper investigates the use of a hybrid neurocomputing approach to detect and then recognise images. The first technique creates and trains intelligent agents capable of detecting face images by using a Generalised Regression Neural Network (GRNN). The second technique further refines the search by recognising images from the detected data set using a feed forward backpropagation neural network. These two agents make up the ‘Detection Agent’ and the ‘Recognition Agent’ in an agent architecture that collaborates with each other to detect and then recognise certain images. The overall agent architecture will operate as an ‘Automatic Target Recognition’ (ATR) system. The architecture of ATR system is presented in this paper and it is shown how the Detection and Recognition Agents (DRA) fit into the overall system. Experiments and results using the DRA are also presented.

/pdf/collaborative-agent-learning-using-hybrid-neurocomputing-4qrggnmuk0.pdf

Lakhmi C. Jain

Papers

A temporal neurofuzzy model for rule-based systems

Advances in Intelligent Image Analysis

A Conceptual Model for Holonic Manufacturing Execution

Electronic Tutorials - Interactive and Attractive

Collaborative agent learning using hybrid neurocomputing.