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

In this chapter we work through a number of techniques raised in previous chapters. Each of the examples is accompanied with a brief explanation, although the syntax within some listings have been abbreviated to simplify the description.We introduced both static and dynamic methods associated with deriving results. A number of algorithms are then explained before discussing the MVC pattern associated with the Sudoku solver application.

Problem Solving Workshop

Multimedia services is the term chosen to describe services which rely on the coordinated and secure storage, processing, transmission, and retrieval of information which exists in various forms [1]. The term refers to the several levels of data processing. It includes application areas, such as digital libraries, e-learning, e-government, e-commerce, e-entertainment, e-health, and e-legal services. In our earlier book [2], we covered aspects of low level data processing of multimedia services in intelligent environments, including storage, recognition, classification, transmission, retrieval, and securing of information. Four additional chapters in [2] considered systems developed to support intermediate level multimedia processing services. These included noise and hearing monitoring, and measurement, augmented reality, and automated lecture rooms. In addition rights management and licensing were included. The final chapter in [2] was devoted to a high-level intelligent recommender service in scientific digital libraries.

Advances in Multimedia Services in Intelligent Environments – Software Development Challenges and Solutions

In the history of mankind, three revolutions which impact the human life are tool-making revolution, agricultural revolution and industrial revolution. They have transformed not only the economy and civilization but the overall development of the human society. Probably, intelligence revolution is the next revolution, which the society will perceive in the next 10 years. ICCD-2014 covers all dimensions of intelligent sciences, i.e. Intelligent Computing, Intelligent Communication and Intelligent Devices. This volume covers contributions from Intelligent Computing, areas such as Intelligent and Distributed Computing, Intelligent Grid & Cloud Computing, Internet of Things, Soft Computing and Engineering Applications, Data Mining and Knowledge discovery, Semantic and Web Technology, and Bio-Informatics. This volume also covers paper from Intelligent Device areas such as Embedded Systems, RFID, VLSI Design & Electronic Devices, Analog and Mixed-Signal IC Design and Testing, Solar Cells and Photonics, Nano Devices and Intelligent Robotics.

Intelligent Computing, Communication and Devices: Proceedings of ICCD 2014, Volume 1

Industry, Artificial Intelligence in

This book at hand explores emerging scientific and technological areas in which Intelligent Computing Systems provide efficient solutions and, thus, may play a role in the years to come. It demonstrates how Intelligent Computing Systems make use of computational methodologies that mimic nature-inspired processes to address real world problems of high complexity for which exact mathematical solutions, based on physical and statistical modelling, are intractable. Common intelligent computational methodologies are presented including artificial neural networks, evolutionary computation, genetic algorithms, artificial immune systems, fuzzy logic, swarm intelligence, artificial life, virtual worlds and hybrid methodologies based on combinations of the previous. The book will be useful to researchers, practitioners and graduate students dealing with mathematically-intractable problems. It is intended for both the expert/researcher in the field of Intelligent Computing Systems, as well as for the general reader in the fields of Artificial and Computational Intelligence who wishes to learn more about the field of Intelligent Computing Systems and its applications. An extensive list of bibliographic references at the end of each chapter guides the reader to probe further into application area of interest to him/her.

Lakhmi C. Jain

Papers

Problem Solving Workshop

Advances in Multimedia Services in Intelligent Environments – Software Development Challenges and Solutions

Intelligent Computing, Communication and Devices: Proceedings of ICCD 2014, Volume 1

Industry, Artificial Intelligence in

Intelligent Computing Systems: Emerging Application Areas