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

Social Network Analysis

国際生活機能分類(International Classification of Functioning, Disability and Health: ICF)にみた福祉・保健・医療の専門職協働における連携に関する貢献と課題

An assessment of the equilibrium dynamics of biomolecular systems, and in particular their most cooperativefluctuations accessibleundernative state conditions, is a first step towards understanding molecular mechanisms relevant to biological function. We present a web-based system, oGNM that enables users to calculate online the shape and dispersion of normal modes of motion for proteins, oligonucleotides and their complexes, or associated biological units, using the Gaussian Network Model (GNM). Computations with the new engine are 5–6 orders of magnitude faster than those using conventional normal mode analyses. Two cases studies illustrate the utility of oGNM. The first shows that the thermal fluctuations predicted for 1250 non-homologous proteins correlate well with X-ray crystallographic data over a broad range [7.3–15 A u ] of inter-residue interaction cutoff distances and the correlations improve with increasing observation temperatures. The second study, focused on 64 oligonucleotides and oligonucleotide–protein complexes, shows that good agreement with experiments is achieved by representing each nucleotide by three GNM nodes (as opposed to one-node-per-residue in proteins) along with uniform interaction ranges for all components of the complexes. These results open the way to a rapid assessment of the dynamics of DNA/ RNA-containing complexes. The server can be accessed at http://ignm.ccbb.pitt.edu/GNM_Online_ Calculation.htm.

/pdf/ognm-online-computation-of-structural-dynamics-using-the-2saagefwsc.pdf

oGNM: online computation of structural dynamics using the Gaussian Network Model

This Editorial lead article for the Journal of Location Based Services surveys this complex and multi-disciplinary field and identifies the key research issues. Although this field has produced early commercial disappointments, the inevitability that pervasive location-aware services on mobile devices will emerge means that much research is needed to inform these developments. The article reviews firstly: the science and technology of positioning, geographic information science, mobile cartography, spatial cognition and interfaces, information science, ubiquitous computing; and secondly the business, content and legal, social and ethics aspects, before synthesising the key issues for this new field.

A critical evaluation of location based services and their potential

Motivation: The knowledge of protein structure is not sufficient for understanding and controlling its function. Function is a dynamic property. Although protein structural information has been rapidly accumulating in databases, little effort has been invested to date toward systematically characterizing protein dynamics. The recent success of analytical methods based on elastic network models, and in particular the Gaussian Network Model (GNM), permits us to perform a high-throughput analysis of the collective dynamics of proteins.

Results: We computed the GNM dynamics for 20 058 structures from the Protein Data Bank, and generated information on the equilibrium dynamics at the level of individual residues. The results are stored on a web-based system called iGNM and configured so as to permit the users to visualize or download the results through a standard web browser using a simple search engine. Static and animated images for describing the conformational mobility of proteins over a broad range of normal modes are accessible, along with an online calculation engine available for newly deposited structures. A case study of the dynamics of 20 non-homologous hydrolases is presented to illustrate the utility of the iGNM database for identifying key residues that control the cooperative motions and revealing the connection between collective dynamics and catalytic activity.

Availability: http://ignm.ccbb.pitt.edu/

Contact:[email protected] and [email protected]

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i GNM: a database of protein functional motions based on Gaussian Network Model

This article reviews a selected set of location-based services (LBS) that have been published in the research literature, focussing on mobile guides, transport support, gaming, assistive technology and health. The research needs and opportunities in each area are evaluated and the connections between each category of LBS are discussed. The review illustrates the enormous diversity of forms in which LBS are appearing and the wide range of application sectors that are represented. However, very few of these applications are implemented pervasively on a commercial basis as this is still challenging technically and economically.

Applications of location-based services: a selected review

Geoinformatics is the science and technology of gathering, analyzing, interpreting, distributing, and using geospatial information. It encompasses a broad range of disciplines brought together to create a detailed but understandable picture of the physical world and our place in it. "The Handbook of Research on Geoinformatics" is the first reference work to map this exciting interdisciplinary field, discussing the complete range of contemporary research topics such as computer modeling, geometry, geoprocessing, and geographic information systems. This expansive reference work covers the complete range, of geoinformatics related issues, trends, theories, technologies, and applications. Following are the features: 42 authoritative contributions by 67 of the world's leading experts in geoinformatics; comprehensive coverage of each specific topic, highlighting recent trends and describing the latest advances in the field; more than 925 references to existing literature and research on geoinformatics; a compendium of over 300 key terms with detailed definitions; organized by topic and indexed, making it a convenient method of reference for all IT/IS scholars and professionals; and, cross-referencing of key terms, figures, and information pertinent to geoinformatics.

/pdf/handbook-of-research-on-geoinformatics-5fefraad1q.pdf

Hassan A. Karimi

Papers

oGNM: online computation of structural dynamics using the Gaussian Network Model

A critical evaluation of location based services and their potential

i GNM: a database of protein functional motions based on Gaussian Network Model

Applications of location-based services: a selected review

Handbook of Research on Geoinformatics