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

Advances in automation and especially artificial intelligence have enabled the formation of rather unique teams with human and (electronic) machine members. This paper proposes a conceptual framework for teaming human and machine. The basis of this framework will be the introduction of the machine into the traditional situation where the human is solely responsible for managing, control and execution of all activities. Focus will be on the identification and classification of activities to be allocated to the machine. Task management and coordination between human and machine will be identified as a specific area of research and design concern.

Teaming Human and Machine: A Conceptual Framework

Multimedia services are now commonly used in various activities in the daily lives of humans. Related application areas include services that allow access to large depositories of information, digital libraries, e-learning and e-education, e-government and e-governance, e-commerce and e-auctions, e-entertainment, e-health and e-medicine, and e-legal services, as well as their mobile counterparts (i.e., m-services). Despite the tremendous growth of multimedia services over the recent years, there is an increasing demand for their further development. This demand is driven by the ever-increasing desire of society for easy accessibility to information in friendly, personalized and adaptive environments. In this book at hand, we examine recent Recommendation Services. Recommendation services appear in the mobile environment, medicine/biology, tourism, education, and so on. The book includes ten chapters, which present various recently developed recommendation services.This research book is directed to professors, researchers, application engineers and students of all disciplines who are interested in learning more about recommendation services, advancing the corresponding state of the art and developing innovative recommendation services.

Multimedia Services in Intelligent Environments: Recommendation Services

This paper presents a novel system integration technical risk assessment model (SITRAM), which is based on Bayesian belief networks (BBN) coupled with parametric models (PM). This model provides statistical information for decision makers, improving risk management of complex projects. System integration technical risks (SITR) represent a significant part of project risks associated with the development of large software intensive systems for defense and commercial applications. We propose a conceptual modeling framework to address the problem of SITR assessment in the early stages of a system life cycle. Initial risks' taxonomy and risks' interrelations have been identified using a hierarchical holographic modeling (HHM) approach. The framework includes a set of BBN models, representing relations between risk contributing factors, and complementing PMs, used to provide input data to the BBN models. In this paper, we present the rationale and the modeling objectives, and describe the concepts and details of BBN experimental model design and implementation. To address practical limitations, heuristic techniques have been proposed for easing the generation of conditional probability tables. PM design principles are described and examples are presented. In conclusion, we summarize the benefits and constraints of SITR assessment based on BBN models. Further research directions and model improvements are also presented.

Systems' Integration Technical Risks' Assessment Model (SITRAM)

Subspace Methods for Pattern Recognition in Intelligent Environment

Trends in Data Mining and Knowledge Discovery Advanced Methods for the Analysis of Semiconductor Manufacturing Process Data Clustering and visualization of Retail Market Baskets Segmentation Of Continuous Data Streams Based on a Change Detection Methodology Instance Selection Using Evolutionary Algorithms: An Experimental Study Using Cooperative Coevolution for Data Mining of Bayesian Networks Knowledge Discovery and Data Mining in Medicine Satellite Image Classification Using Cascaded Architecture of Neural Fuzzy Network Discovery of Positive and Negative Rules from Medical Databases based on Rough Sets

Lakhmi C. Jain

Papers

Teaming Human and Machine: A Conceptual Framework

Multimedia Services in Intelligent Environments: Recommendation Services

Systems' Integration Technical Risks' Assessment Model (SITRAM)

Subspace Methods for Pattern Recognition in Intelligent Environment

Advanced techniques in data mining and knowledge discovery