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

Training Radial Basis Function Neural Networks through Parabolic Evolutionary Algorithm.

Intelligent Decision Technologies: Proceedings of the 4th International Conference on Intelligent Decision Technologies (IDT'2012) - Volume 2 ABC

This chapter provides a broad overview of machine learning (ML) paradigms both emerging as well as well-established ones These paradigms include: Bayesian Learning, Decision Trees, Granular Computing, Fuzzy and Rough Sets, Inductive Logic Programming, Reinforcement Learning, Neural Networks and Support Vector Machines In addition, challenges in ML such as imbalanced data, perceptual computing, and pattern recognition of data which is episodic as well as temporal are also highlighted

Emerging Paradigms in Machine Learning: An Introduction

Agents and multi-agent systems are related to a modern software paradigm which has long beenrecognized as a promising technology for constructingautonomous, complex andintelligent systems. Thetopics covered in this volume include agent-oriented software engineering, agent co-operation, co-ordination, negotiation, organization and communication, distributed problem solving, specification of agentcommunication languages, agentprivacy, safety and security, formalization of ontologies and conversational agents. The volume highlights new trends andchallenges in agent and multi-agent research and includes 38 papers classifiedin the following specific topics:learningparadigms, agent-based modeling and simulation, business model innovation anddisruptive technologies, anthropic-oriented computing, serious games andbusiness intelligence, design and implementation of intelligent agentsand multi-agent systems, digital economy, and advances in networkedvirtual enterprises. Published papershave been presented at the 9th KES Conference on Agent and Multi-Agent Systems Technologies and Applications(KES-AMSTA 2015) held in Sorrento, Italy. Presented results should be of value tothe research community working in the fields of artificial intelligence, collective computational intelligence, robotics, dialogue systems and, inparticular, agent and multi-agent systems, technologies, tools and applications.

Agent and Multi-Agent Systems: Technologies and Applications 9th KES International Conference, KES-AMSTA 2015 Sorrento, Italy, June 2015, Proceedings

Multi-agent teaming is an active research field of multi-agent systems Flexible multi-agent decision making requires effective reaction and adaption to dynamic changes under time pressure, especially in real-time and dynamic systems The joint intension and sharedplan are two most popular theories for the teamwork of multi-agent systems However, there is no clear guidance for designing and implementing agents' teaming BDI (Belief, Desire, and Intension) architecture has been widely used to design multi-agent systems In this paper, a role-based BDI framework is presented to facilitate the team level optimization problems such as competitive, cooperation and coordination problems This BDI framework is extended on the commercial agent software development environment known as JACK Teams The layered architecture has been used to group the agents' competitive and cooperative behaviors In addition, we present the reinforcement learning techniques to learn different behaviors through experience These issues have been specified and investigated within a real-time 2D simulation environment known as soccerBots

Lakhmi C. Jain

Papers

Training Radial Basis Function Neural Networks through Parabolic Evolutionary Algorithm.

Intelligent Decision Technologies: Proceedings of the 4th International Conference on Intelligent Decision Technologies (IDT'2012) - Volume 2 ABC

Emerging Paradigms in Machine Learning: An Introduction

Agent and Multi-Agent Systems: Technologies and Applications 9th KES International Conference, KES-AMSTA 2015 Sorrento, Italy, June 2015, Proceedings

A role-oriented BDI framework for real-time multiagent teaming