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 book constitutes the refereed proceedings of the 16th International Conference on Knowledge-Based and Intelligent Information and Engineering Systems, KES 2012, held in San Sebastian, Spain, in September 2012. The 20 revised full papers presented were carefully reviewed and selected from 130 submissions. The papers are organized in topical sections on bioinspired and machine learning methods, machine learning applications, semantics and ontology based techniques, and lattice computing and games.

Knowledge Engineering, Machine Learning and Lattice Computing with Applications: 16th International Conference, KES 2012, San Sebastian, Spain, ... / Lecture Notes in Artificial Intelligence)

Vision-Based Autonomous Robot Navigation.

Use of multi-category proximal SVM for data set reduction.- Bayesian control of dynamic systems.- AppART: a hybrid neural network based on adaptive resonance theory for universal function approximation.- An algorithmic approach to the main concepts of rough set theory.- Automated case selection from databases using similarity-based rough approximation.- An induction algorithm with selection significance based on a fuzzy derivative.- Model and fixpoint semantics for fuzzy disjunctive programs with weak similarity.- An automated report generation tool for the data understanding phase.- Finding trigonometric identities with tree adjunct grammar guided genetic Modeling a distributed knowledge management for autonomous cooperative agents with knowledge migration programming.- Intelligent information systems based on paraconsistent logic programs.- Neuro-fuzzy paradigms for intelligent energy management.- Information space optimization for inductive learning.- Detecting, tracking, and classifying human movement using active contour Fuzzy sets in investigation of human cognition processes models and neural networks.- A full explanation facility for an MLP network that classifies .- Automatic translation to controlled medical vocabularies low-back-pain patients and for predicting MLP reliability.- A genetic programming for the induction of natural language parser.

Innovations in intelligent systems

This paper proposes a novel chaotic reaction-diffusion model of cellular tumor growth and metastasis. The model is based on the multiscale diffusion cancer-invasion model (MDCM) and formulated by introducing strong nonlinear coupling into the MDCM. The new model exhibits temporal chaotic behavior (which resembles the classical Lorenz strange attractor) and yet retains all the characteristics of the MDCM diffusion model. It mathematically describes both the processes of carcinogenesis and metastasis, as well as the sensitive dependence of cancer evolution on initial conditions and parameters. On the basis of this chaotic tumor-growth model, a generic concept of carcinogenesis and metastasis is formulated. 
Keywords: reaction-diffusion tumor growth model, chaotic attractor, sensitive dependence on initial tumor characteristics

Lakhmi C. Jain

Papers

Knowledge Engineering, Machine Learning and Lattice Computing with Applications: 16th International Conference, KES 2012, San Sebastian, Spain, ... / Lecture Notes in Artificial Intelligence)

Vision-Based Autonomous Robot Navigation.

Innovations in intelligent systems

A Theoretical Model of Chaotic Attractor in Tumor Growth and Metastasis

Applied Intelligent Systems