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

What is modal logic

A system and method are provided for transmitting data from a server to a client via blocks. A user query for a solution set is transmitted to a server, which is then used to query a database for a first subset of the solution set. A first block of data is built from data in the database of the server, containing a subset of the solution set, and is then sent to the client over a network. When a second request from the client for the data in the database of the server is received, a second block of the data sets is queried from the database, built, and then transmitted to the client over the network.

Block-based communication in a communication services patterns environment

EDITORIAL BOARD Robert Davison Aviles, Bradley University Harley E. Baker, California State University–Channel Islands Jean-Guy Blais, Universite de Montreal, Canada Catherine Y. Chang, Georgia State University Robert C. Chope, San Francisco State University Kevin O. Cokley, University of Missouri, Columbia Patricia B. Elmore, Southern Illinois University Shawn Fitzgerald, Kent State University John J. Fremer, Educational Testing Service Vicente Ponsoda Gil, Universidad Autonoma de Madrid, Spain Jo-Ida C. Hansen, University of Minnesota Charles C. Healy, University of California at Los Angeles Robin K. Henson, University of North Texas Flaviu Adrian Hodis, Victoria University of Wellington, New Zealand Janet K. Holt, Northern Illinois University David A. Jepsen, The University of Iowa Gregory Arief D. Liem, National Institute of Education, Nanyang Technological University Wei-Cheng J. Mau, Wichita State University Larry Maucieri, Governors State College Patricia Jo McDivitt, Data Recognition Corporation Peter F. Merenda, University of Rhode Island Matthew J. Miller, University of Maryland Ralph O. Mueller, University of Hartford Jane E. Myers, The University of North Carolina at Greensboro Philip D. Parker, University of Western Sydney Ralph L. Piedmont, Loyola College in Maryland Alex L. Pieterse, University at Albany, SUNY Nicholas J. Ruiz, Winona State University James P. Sampson, Jr., Florida State University William D. Schafer, University of Maryland, College Park William E. Sedlacek, University of Maryland, College Park Marie F. Shoffner, University of Virginia Len Sperry, Florida Atlantic University Kevin Stoltz, University of Mississippi Jody L. Swartz-Kulstad, Seton Hall University Bruce Thompson, Texas A&M University Timothy R. Vansickle, Minnesota Department of Education Steve Vensel, Palm Beach Atlantic University Dan Williamson, Lindsey Wilson College F. Robert Wilson, University of Cincinnati

measurement and evaluation in counseling and Development

A system, method and article of manufacture are provided for assigning a view to an activity. Notification is received that a startup event of an activity has occurred. A reference to a first instance of an object created by the startup event of the activity is also received. A view to launch is determined in response to the receipt of the notification and the reference. The view is based on predetermined criteria. The view is associated with the activity and displayed.

A view configurer in a presentation services patterns enviroment

Many maintenance decisions require the evaluation of alternative solutions in terms of complex maintenance criteria such as cost, repairability, reliability and availability requirements. Such problems can be formulated as multi‐criteria decision‐making problems. The relative importance of maintenance criteria is difficult to assess, and therefore a sensitivity analysis becomes a necessity. The sensitivity analysis approach presented reveals some counter‐intuitive results and can considerably enhance the application of decision analysis in complex maintenance management.

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Determining the most important criteria in maintenance decision making

Foreword G. Piatetsky-Shapiro. Preface. Acknowledgements. 1. The Scope and Methods of the Study. 2. Numerical Data Mining Models with Financial Applications. 3. Rule-Based and Hybrid Financial Data Mining. 4. Relational Data Mining (RDM). 5. Financial Applications of Relational Data Mining. 6. Comparison of Performance of RDM and other methods in financial applications. 7. Fuzzy logic approach and its financial applications. References. Subject Index.

Data Mining in Finance: Advances in Relational and Hybrid Methods

http://bit.csc.lsu.edu/trianta/Journal_PAPERS1/fcancr1.pdf

Fuzzy logic in computer-aided breast cancer diagnosis: analysis of lobulation.

Data mining in finance

Preface, Color Plates PART 1. Visual Problem Solving and Decision Making 1. Decision process and its visual aspects, 2.Information visualization value stack model PART 2. Visual and Heterogeneous Reasoning 3. Visual reasoning and representation, 4. Representing visual decision making: a computational architecture for heterogeneous reasoning, 5. Algebraic visual symbolism for problem solving: iconic equations from Diophantus to our days, 6. Iconic reasoning architecture for analysis and decision making, 7.Toward visual reasoning and discovery: lessons from early history of mathematics PART 3. Visual Correlation 8. Visual correlation methods and models, 9.Iconic approach for data annotating, searching and correlating, 10. Bruegel iconic correlation system PART 4. Visual and Spatial Data Mining 11. Visualizing data streams, 12. SPIN! - an enterprise architecture for data mining and visual analysis of spatial data, 13. XML-based visualization and evaluation of data mining results, 14. Neural-network techniques for visual mining clinical electronencephalograms, 15. Visual data mining with simultaneous rescaling, 16. Visual data mining using monotone Boolean functions PART 5. Visual and Spatial Problem Solving in Geospatial Domains 17. Imagery integration as conflict resolution decision process: methods and approaches, 18. Multilevel analytical and visual decision framework for imagery conflation and registration, 19. Conflation of images with algebraic structures, 20. Algorithm development technology for conflation and area-based conflation algorithm, 21. Virtual experts for imagery registration and conflation

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Boris Kovalerchuk

Papers

Determining the most important criteria in maintenance decision making

Data Mining in Finance: Advances in Relational and Hybrid Methods

Fuzzy logic in computer-aided breast cancer diagnosis: analysis of lobulation.

Data mining in finance

Visual and spatial analysis : advances in data mining, reasoning, and problem solving