Lakhmi C. Jain

Bio: Lakhmi C. Jain is an academic researcher from University of Technology, Sydney. The author has contributed to research in topics: Artificial neural network & Intelligent decision support system. The author has an hindex of 41, co-authored 419 publications receiving 10015 citations. Previous affiliations of Lakhmi C. Jain include University of South Australia & University of Canberra.

An Agent-Oriented Approach for Image Classification With Ica-Color Sift

Abstract: This paper discusses the research conducted on developing a Multi-Agent System (MAS) for solving an image classification task. The aim of this research is to equip agents in MAS with reusable autonomous capabilities. The system provides a flexible framework for developing the communication aspects within an agent-oriented architecture to program agents that dynamically acquire functionality at runtime using event based messaging. In this research agents are equipped with unique image processing capabilities and required to interact and cooperate to achieve the goal. Complementary research on a variety of agent tools (specifically JACK, JADE and CIAgent) and communication languages (ACL, KQML, FIPA and SOAP) has been reviewed to glean knowledge that enables these agents to adapt those capabilities. The system has generated encouraging results.

Innovations in intelligent agents and web

Abstract: Intelligent agents are an integral and expanding part of practical systems. The ability of agents to generate goals and determine whether to accept the goals of others provides a powerful approach to autonomous computing, particularly in Web-based systems. This invited session of the 10th International Conference on Knowledge-Based and Intelligent Information and Engineering Systems focuses on innovations in intelligent agents and Web-based agent systems.

Asymmetric Clustering Based on Self-Similarity

Abstract: This paper proposes a clustering method for asymmetric similarity data. In this method, systematic asymmetry in the data is explained by using self-similarity of objects. We exploit an additive fuzzy clustering model for capturing the classification structure in the data. Moreover, the symmetric similarity data is restored by using the result of the clustering method. Therefore, we can exploit many data analyses in which objective data is symmetric similarity data. Several numerical examples are shown in order to show the better performance of the proposed method.

Guest Editorial: Special issue on advances in computational intelligence paradigms and applications

Abstract: Computational Intelligence (CI) is an emerging field covering a highly interdisciplinary methodological framework that is useful for supporting the design, analysis, and deployment of intelligent systems. According to Bezdek (1994), “. . . a system is computationally intelligent when it: deals only with numerical (low-level) data, has a pattern recognition component, and does not use knowledge in the AI sense; and additionally when it (begins to) exhibit (i) computational adaptivity; (ii) computational fault tolerance; (iii) speed approaching human-like turnaround, and (iv) error rates that approximate human performance . . .”. Indeed, CI involves innovative models with a high level of machine learning quotient that combine elements of learning, adaptation, and evaluation. Examples of CI paradigms include fuzzy computing, neural computing, evolutionary computing, probabilistic computing, rough set theory, knowledge-based systems, adaptive learning algorithms, and hybrids of these paradigms. These techniques can be applied to a wide range of problems including optimization, decision making, information processing, pattern recognition, and intelligent data analysis. In this special issue, a number of papers that address theoretical advances as well as practical applications of various CI techniques are presented. The first two papers cover investigation into fuzzy measures and intelligent data analysis. The next two

Machine learning

Abstract: 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.).

Pattern Recognition and Machine Learning

Abstract: 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.

The knowledge-creating company : how Japanese companies create the dynamics of innovation

Abstract: 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.