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

This section describes a number of geometrical and intensity procedures which are frequently used in the processing of solar images. Geometrical operations have a range of applications from compensating for different sizes and viewpoints of images, to displaying an image in a different co-ordinate system. Intensity operations ranging from removing radial and non-radial background illumination variations to removing dust lines; image de-blurring can be useful preliminary steps before the application of feature detection algorithms. 2.1.1 Geometrical Standardization This section describes procedures which are used to change the shape or remap a digital image by applying geometrical transformations such as the one shown in Fig. 2.1. A common approach, used when the transformation is invertible, is to first apply the forward transformation to define the space occupied by the new transformed image. This space is then scanned pixel by pixel, and for each pixel the inverse transformation is applied to determine where it came from in the original image. This position generally falls between the integer pixel locations in the original position and an interpolation procedure is applied to determine the appropriate image value at that point. “Image Resampling and Interpolation” describes in detail this commonly used approach to applying a geometrical transformation to an image and “Removal of Nonradial Background Illumination and Removal of Dust Lines” describe specific examples in which different geometrical transformations are applied to solar images. www.springerlink.com © Springer-Verlag Berlin Heidelberg 2007 S.S. Ipson et al.: Image Standardization and Enhancement, Studies in Computational Intelligence (SCI) 46, 19–58 (2007) Image Resampling and Interpolation The general mathematical form of a continuous geometrical transformation applied to an image is ) , ( ) , ( y x g y y x f x = ′ = ′ , (2.1) where the image intensity at the point (x, y) in the original image has been moved to the point (x′, y′) in the transformed image under the functions f and g. A simple case is the affine transformation (illustrated in Fig. 2.1) which can be used to standardize the position and size of a solar image and is sometimes used to correct the shape of the solar disc from an instrumentally distorted elliptical shape back to the true circular shape. The affine transformation is expressed in matrix form with homogeneous coordinates as follows:

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Artificial Intelligence in Recognition and Classification of Astrophysical and Medical Images

ANFISunfoldedintime for multivariate time series forecasting

Knowledge processing and decision making in agent-based systems constitute the key components of intelligent machines. The contributions included in the book are: Innovations in Knowledge Processing and Decision Making in Agent-Based Systems Towards Real-World HTN Planning Agents Mobile Agent-Based System for Distributed Software Maintenance Software Agents in New Generation Networks: Towards the Automation of Telecom Processes Multi-agent Systems and Paraconsistent Knowledge An Agent-based Negotiation Platform for Collaborative Decision-Making in Construction Supply Chain An Event-Driven Algorithm for Agents at the Web A Generic Mobile Agent Framework Toward Ambient Intelligence Developing Actionable Trading Strategies Agent Uncertainty Model and Quantum Mechanics Representation Agent Transportation Layer Adaptation System Software Agents to Enable Service Composition through Negotiation Advanced Technology Towards Developing Decentralized Autonomous Flexible Manufacturing Systems

Knowledge Processing and Decision Making in Agent-Based Systems

Computational Intelligence in Fault Diagnosis (Advanced Information and Knowledge Processing)

Fuzzy Control Methodology: Basics and State of the Art, Toshio Fukuda and Nfaoyuki Learning Eye-Arm Coordination Using Neural and Fuzzy Neural Techniques, Adrian Stoica Learning Stiffness Characteristics of the Human Hand Using a Neuro-Fuzzy System, Alexander Iliesh and Abraham Kandel Fuzzy Reduction Control of Acceleration and Vibration of a Stretcher-Cart on an Ambulance, Mikio Maeda and Shuta Murakami Computational Intelligence for Medical Information Analysis and Refinement, Patrik Eklund, Lena Kallin, and Gustaf Seln Intelligent Diagnostic Systems in Maternal and Fetal Medicine, Sinan Beksac, Ayden Erkmen, Sezer Aksel, Ugur Halici, Kemal Leblebicioglu, Meral Beksac, Volkan Atalay, and Lakhmi C. Jain Wavelet Network with Convex Wavelets: Applications to Nonlinear System Modeling and Feature Extraction in Vectorcardiography, Eiji Uchino, Takashi Samatsu, and Takeshi Yamakawa Features-Oriented Filtering of Biological Signals, Horia-Nicolai Teodorescu and Cristian Bonciu An Emotion Processing System Based on Fuzzy Inference and Subjective Observations, Naruki Shirahama, Torao Yanaru, and Masahiro Nagamatsu Fuzzy Methods in Nutrition Planning and Education and in Clinical Nutrition, Bernd Wirsam and Andreas Hahn Designing Experiments for Medical Diagnosis Evaluation Using Membership Functions, Jose Luis Verdegay, and Alejandro Sancho-Royo

Lakhmi C. Jain

Papers

Artificial Intelligence in Recognition and Classification of Astrophysical and Medical Images

ANFISunfoldedintime for multivariate time series forecasting

Knowledge Processing and Decision Making in Agent-Based Systems

Computational Intelligence in Fault Diagnosis (Advanced Information and Knowledge Processing)

Soft computing in human-related sciences