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 extraction of a co-authorship network from a set of bibliographic records in which articles and authors are uniquely identified is an easily solvable problem. However, in a vast majority of bibliographic databases authors are identified by their names. This causes the problem of correct identification of nodes in co-authorship networks due to ambiguous author names. In this chapter we present an overview of initial-based, heuristic and machine learning approaches to the name disambiguation problem. Then, we study the performance of various string similarity measures for detecting name synonyms in bibliographic records. After that, we propose a novel method for disambiguating author names that is based on reference similarity networks and community detection techniques. Finally, we present a case study investigating the impact of name disambiguation on the structure of co-authorship networks.

Extraction of Co-authorship Networks

This paper presents an approach for modelling Systems Integration Technical Risks SITR assessment using Bayesian Belief Networks BBN. SITR represent a significant part of project risks associated with a development of large software intensive systems. We propose conceptual modelling framework to address the problem of SITR assessment at early stages of a system life cycle. This framework includes a set of BBN models, representing the risk contributing factors, and complementing Parametric Models PM, used for providing input data to the BBN models. In particular we describe SITR identification approach explaining corresponding BBN models' topologies and relevant conceptual model framework. This framework includes a set of BBN models, representing the risk contributing factors, fused with complementary PMs providing input data to the BBN models. Heuristic approaches for easing Conditional Probabilities Tables CPT generation are described. We briefly discuss preliminary results of model testing. In conclusion we summarise benefits and constraints for SITR assessment based on BBN models, and provide suggestions for further research directions for model improvement.

The Systems Integration Technical Risk assessment fusing of Bayesian Belief Networks and Parametric Models

Watermarking is one useful solution for digital rights management (DRM) systems In this paper, in addition to following conventional objectives to conquer against intentional or unintentional attacks, we not only watch the survivability of embedded watermark, but also focus on retaining the watermarked image quality with the aid of multiple description coding (MDC) MDC is an algorithm for error resilient coding, suitable for transmitting compressed data over multiple channels In this paper, we propose an innovative algorithm on vector quantization (VQ) based image watermarking, which is suitable for error-resilient transmission over noisy channels By incorporating watermarking with multiple description coding, the scheme we proposed for embedding three watermarks can effectively overcome channel impairments while retaining the capability for copyright and ownership protection With the promising simulation results presented, we can demonstrate the utility and practicability of our algorithm

The triple-watermarking algorithm with multiple description coding over lossy communication networks

Complex network analysis is a collection of quantitative methods for studying the structure and dynamics of complex networked systems. This chapter presents the fundamentals of complex network analysis. We start out by presenting the basic concepts of complex networks and graph theory. Then, we focus on fundamental network analysis measures and algorithms related to node connectivity, distance, centrality, similarity and clustering. Finally, we discuss fundamental complex network models and their characteristics.

Fundamentals of Complex Network Analysis

This paper presents a proposal of an integration of fuzzy and two-valued logic, based on natural language semantics. We introduce a simple semantics of natural language which can treat fuzzy and two-valued logics, both of which are essential in natural language sentences. As an important example, this paper focuses on spatial relations, or scene generation system which synthesizes images of the meaning derived from natural language semantics. In these cases, as discussed below, it is better not to treat two-valued logic as a special case of fuzzy logic.

Lakhmi C. Jain

Papers

Extraction of Co-authorship Networks

The Systems Integration Technical Risk assessment fusing of Bayesian Belief Networks and Parametric Models

The triple-watermarking algorithm with multiple description coding over lossy communication networks

Fundamentals of Complex Network Analysis

An Integration of Fuzzy and Two-Valued Logics on Natural Language Semantics