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

Previous research on human-machine teaming[1] argued that a formal specification in human-agent systems could prove beneficial in a hostile environment, and proposed an initial demonstration which has been successfully implemented[2] using a test-bed based on JACK[3] and Unreal Tournament (UT) [4]. This paper describes how to harvest these results and proposes a team for situations where human-agent collaboration is crucial to its success. A specific game type within Unreal Tournament is utilised, called Capture The Flag[5]. The proposed team is designed with the Prometheus agent design methodology[6] as a guide and is comprised of humans and agents each having a specific role.

Teaming Humans and Agents in a Simulated World

This research book presents some of the most recent advances in neural information processing models including both theoretical concepts and practical applications. The contributions include: Advances in neural information processing paradigms - Self organizing structures - Unsupervised and supervised learning of graph domains - Neural grammar networks - Model complexity in neural network learning - Regularization and suboptimal solutions in neural learning - Neural networks for the classification of vectors, sequences and graphs - Metric learning for prototype-based classification - Ensembles of neural networks - Fraud detection using machine learning - Computational modeling of neural multimodal integration. This book is directed to the researchers, graduate students, professors and practitioner interested in recent advances in neural information processing paradigms and applications.

Innovations in Neural Information Paradigms and Applications

IDT (Intelligent Decision Technologies) seeks an interchange of research on intelligent systems and intelligent technologies which enhance or improve decision making in industry, government and academia The focus is interdisciplinary in nature, and includes research on all aspects of intelligent decision technologies, from fundamental development to the applied system It constitutes a great honor and pleasure for us to publish the works and new research results of scholars from the First KES International Symposium on Intelligent Decision Technologies (KES IDT'09), hosted and organized by University of Hyogo in conjunction with KES International (Himeji, Japan, April, 2009) The symposium was concerned with theory, design, development, implementation, testing and evaluation of intelligent decision systems Its topics included intelligent agents, fuzzy logic, multi-agent systems, artificial neural networks, genetic algorithms, expert systems, intelligent decision making support systems, information retrieval systems, geographic information systems and knowledge management systems These technologies have the potential to support decision making in many areas of management, international business, finance, accounting, marketing, healthcare, military applications, production, networks, traffic management, crisis response and human interfaces

New Advances in Intelligent Decision Technologies: Results of the First KES International Symposium IDT'09

SL(2,Z) Action on Three-Dimensional Conformal Field Theories with Abelian Symmetry (E Witten) The Search for a Holographic Dual to AdS3xS3xS3xS1 Chern-Simons Gauge Theory and the AdS3/CFT2 Correspondence (S Gukov et al.) Spinning Strings and AdS/FT Duality (A A Tseytlin) TASI 2003 Lectures on AdS/CFT (J Maldacena) The Three-String Vertex for a Plane-Wave Background (J H Schwarz) A New Hat or the c=1 Matrix Model (M R Douglas et al.) Small Instantons, Little Strings and Free Fermions (A Losev et al.) AdS and dS Entropy from String Junctions or the Function of Junction Conjunctions (E Silverstein) Low-Dimensional Sisters of Seiberg-Witten Effective Theory (A V Smilga) From Super-Yang-Mills Theory to QCD: Planar Equivalence and Its Implications (A Armoni et al.) This is How It Was ... (I Kogan).

Intelligent and watermarking techniques

At the dawn of the 4th Industrial Revolution, data analytics is emerging as a force that drives towards dramatic changes in our daily lives, the workplace and human relationships. Synergies between physical, digital, biological and energy sciences and technologies, brought together by non-traditional data collection and analysis, drive the digital economy at all levels and offer new, previously-unavailable opportunities. The need for data analytics arises in most modern scientific disciplines, including engineering; natural-, computer- and information sciences; economics; business; commerce; environment; healthcare; and life sciences. Coming as the third volume under the general title MACHINE LEARNING PARADIGMS, the book includes an editorial note (Chapter 1) and an additional 12 chapters, and is divided into five parts: (1) Data Analytics in the Medical, Biological and Signal Sciences, (2) Data Analytics in Social Studies and Social Interactions, (3) Data Analytics in Traffic, Computer and Power Networks, (4) Data Analytics for Digital Forensics, and (5) Theoretical Advances and Tools for Data Analytics. This research book is intended for both experts/researchers in the field of data analytics, and readers working in the fields of artificial and computational intelligence as well as computer science in general who wish to learn more about the field of data analytics and its applications. An extensive list of bibliographic references at the end of each chapter guides readers to probe further into the application areas of interest to them.

Lakhmi C. Jain

Papers

Teaming Humans and Agents in a Simulated World

Innovations in Neural Information Paradigms and Applications

New Advances in Intelligent Decision Technologies: Results of the First KES International Symposium IDT'09

Intelligent and watermarking techniques

Machine Learning Paradigms: Advances in Data Analytics