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

Data Mining - Concepts and Techniques.

Data Mining Practical Machine Learning Tools and Techniques

I have developed "tennis elbow" from lugging this book around the past four weeks, but it is worth the pain, the effort, and the aspirin. It is also worth the (relatively speaking) bargain price. Including appendixes, this book contains 894 pages of text. The entire panorama of the neural sciences is surveyed and examined, and it is comprehensive in its scope, from genomes to social behaviors. The editors explicitly state that the book is designed as "an introductory text for students of biology, behavior, and medicine," but it is hard to imagine any audience, interested in any fragment of neuroscience at any level of sophistication, that would not enjoy this book. The editors have done a masterful job of weaving together the biologic, the behavioral, and the clinical sciences into a single tapestry in which everyone from the molecular biologist to the practicing psychiatrist can find and appreciate his or

Principles of Neural Science

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

Summarization is an essential requirement for achieving a more compact and interesting representation of sports video contents. We propose a framework that integrates highlights into play segments and reveal why we should still retain breaks. Experimental results show that fast detections of whistle sounds, crowd excitement, and text boxes can complement existing techniques for play-breaks and highlights localization.

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Highlights for more complete sports video summarization

Long non-coding RNA (lncRNA) have been implicated in diverse biological roles including gene regulation and genomic imprinting Identifying lncRNA in bovine across many differing tissue would contribute to the current repertoire of bovine lncRNA, and help further improve our understanding of the evolutionary importance and constraints of these transcripts Additionally, it could aid in identifying sites in the genome outside of protein coding genes where mutations could contribute to variation in complex traits This is particularly important in bovine as genomic predictions are increasingly used in genetic improvement for milk and meat production Our aim was to identify and annotate novel long non coding RNA transcripts in the bovine genome captured from RNA Sequencing (RNA-Seq) data across 18 tissues, sampled in triplicate from a single cow To address the main challenge in identifying lncRNA, namely distinguishing lncRNA transcripts from unannotated genes and protein coding genes, a lncRNA identification pipeline with a number of filtering steps was developed A total of 9,778 transcripts passed the filtering pipeline The bovine lncRNA catalogue includes MALAT1 and HOTAIR, both of which have been well described in human and mouse genomes We attempted to validate the lncRNA in libraries from three additional cows 726 (8747%) liver and 1,668 (5527%) blood class 3 lncRNA were validated with stranded liver and blood libraries respectively Additionally, this study identified a large number of novel unknown transcripts in the bovine genome with high protein coding potential, illustrating a clear need for better annotations of protein coding genes

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A catalogue of novel bovine long noncoding RNA across 18 tissues.

Developing effective and efficient negotiation mechanisms for real-world applications such as e-business is challenging because negotiations in such a context are characterized by combinatorially complex negotiation spaces, tough deadlines, very limited information about the opponents, and volatile negotiator preferences. Accordingly, practical negotiation systems should be empowered by effective learning mechanisms to acquire dynamic domain knowledge from the possibly changing negotiation contexts. This article illustrates our adaptive negotiation agents, which are underpinned by robust evolutionary learning mechanisms to deal with complex and dynamic negotiation contexts. Our experimental results show that GA-based adaptive negotiation agents outperform a theoretically optimal negotiation mechanism that guarantees Pareto optimal. Our research work opens the door to the development of practical negotiation systems for real-world applications. © 2006 Wiley Periodicals, Inc. Int J Int Syst 21: 41–72, 2006.

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An evolutionary learning approach for adaptive negotiation agents

Background: Drug discovery is the process of discovering and designing drugs, which includes target identification, target validation, lead identification, lead optimization and introduction of the new drugs to the public. This process is very important, involving analyzing the causes of the diseases and finding ways to tackle them. Objective: The problems we must face include: i) that this process is so long and expensive that it might cost millions of dollars and take a dozen years; and ii) the accuracy of identification of targets is not good enough, which in turn delays the process. Introducing bioinformatics into the drug discovery process could contribute much to it. Bioinformatics is a booming subject combining biology with computer science. It can explore the causes of diseases at the molecular level, explain the phenomena of the diseases from the angle of the gene and make use of computer techniques, such as data mining, machine learning and so on, to decrease the scope of analysis and enhance th...

Yi-Ping Phoebe Chen

Papers

Highlights for more complete sports video summarization

A catalogue of novel bovine long noncoding RNA across 18 tissues.

An evolutionary learning approach for adaptive negotiation agents

Identifying targets for drug discovery using bioinformatics

True real time pricing and combined power scheduling of electric appliances in residential energy management system