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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.


Papers
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01 Jan 2011
TL;DR: Experimental results show that the approach to non-local pattern analysis does not always improve the accuracy of mining global patterns in multiple databases, and an approach of non- local pattern analysis (NLPA) is introduced.
Abstract: In many applications we need to synthesize global patterns in multiple large databases, where the applications are independent of the characteristics of local patterns. Pipelined feedback technique (PFT) seems to be the most effective technique under the approach of local pattern analysis (LPA). The goal of this paper is to analyse the effect of database grouping on multi-database mining. For this purpose we design a database grouping algorithm. We introduce an approach of non-local pattern analysis (NLPA) by combining database grouping algorithm and pipelined feedback technique for multi-database mining. We propose to judge the effectiveness of non-local pattern analysis for multi-database mining. We conduct experiments on both real and synthetic databases. Experimental results show that the approach to non-local pattern analysis does not always improve the accuracy of mining global patterns in multiple databases. Index Terms — Local pattern analysis, Multi-database mining, Non-local pattern analysis, Pipelined feedback technique, Synthesis of patterns

3 citations

Book ChapterDOI
01 Jan 2008
TL;DR: This chapter provides a brief introduction to the developments of the Artificial Intelligence field in the evolution of the Web covered in this book.
Abstract: This chapter provides a brief introduction to the developments of the Artificial Intelligence field in the evolution of the Web covered in this book. This chapter presents the organization of this book. A comprehensive list of resources is also provided in the end to help the reader in gaining deep insights of the field.

3 citations

Book ChapterDOI
TL;DR: A role-based BDI framework is presented to facilitate optimization problems at the team level such as competitive, cooperation, and coordination problems, extended on the commercial agent software development environment known as JACK Teams.
Abstract: Agent teaming is a subfield of multi-agent systems that is mainly composed of artificial intelligence and distributed computing techniques. Autonomous agents are required to be able to adapt and learn in uncertain environments via communication and collaboration in both competitive and cooperative situations. The joint intension and sharedPlan are two most popular theories for the teamwork of multi-agent systems. However, there is no clear guideline for designing and implementing agents’ teaming. As a popular cognitive architecture, the BDI (Belief, Desire, and Intension) architecture has been widely used to design multi-agent systems. In this aspect, flexible multi-agent decision making requires effective reactions and adaptation to dynamic environment under time pressure, especially in real-time and dynamic systems. Due to the inherent complexity of real-time, stochastic, and dynamic environments, it is often extremely complex and difficult to formally verify their properties a priori. For real-time, non-deterministic and dynamic systems, it is often difficult to generate enough episodes via real applications for training the goal-oriented agent’s individual and cooperative learning abilities. In this article, a role-based BDI framework is presented to facilitate optimization problems at the team level such as competitive, cooperation, and coordination problems. This BDI framework is extended on the commercial agent software development environment known as JACK Teams. The layered architecture has been used to group the agents’ competitive and cooperative behaviors. In addition, we present the use of reinforcement learning techniques to learn different behaviors through experience. These issues have been investigated and analyzed using a real-time 2D simulation environment known as SoccerBots.

3 citations

Proceedings ArticleDOI
30 Jun 2004
TL;DR: For a multi-user-based watermarking system, a new scheme for providing the function of secret sharing is proposed and a user-key generating procedure is introduced to generate one master key and several normal keys.
Abstract: For a multi-user-based watermarking system, a new scheme for providing the function of secret sharing is proposed. A user-key generating procedure is introduced to generate one master key and several normal keys. By using either of these normal keys, a secret watermark is obtained from the cover image. By referring to the original watermark and all the generated secret watermarks, a public watermark is created and embedded into the cover image. The proposed scheme does not require the original image to be presented during extracting. To reveal the genuine watermark from the watermarked image, except for the super-user who can extract it directly by using the master key, the normal users who share the secret can only achieve it by presenting the shadow watermarks extracted by using their own keys

3 citations


Cited by
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Journal ArticleDOI
TL;DR: Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis.
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.).

13,246 citations

Christopher M. Bishop1
01 Jan 2006
TL;DR: Probability distributions of linear models for regression and classification are given in this article, along with a discussion of combining models and combining models in the context of machine learning and classification.
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.

10,141 citations

Book
01 Jan 1995
TL;DR: In this article, Nonaka and Takeuchi argue that Japanese firms are successful precisely because they are innovative, because they create new knowledge and use it to produce successful products and technologies, and they reveal how Japanese companies translate tacit to explicit knowledge.
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.

7,448 citations

01 Jan 2009

7,241 citations