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Machine Learning (machine + learning)
Terms modified by Machine Learning Selected AbstractsAPPLYING MACHINE LEARNING TO LOW-KNOWLEDGE CONTROL OF OPTIMIZATION ALGORITHMSCOMPUTATIONAL INTELLIGENCE, Issue 4 2005Tom Carchrae This paper addresses the question of allocating computational resources among a set of algorithms to achieve the best performance on scheduling problems. Our primary motivation in addressing this problem is to reduce the expertise needed to apply optimization technology. Therefore, we investigate algorithm control techniques that make decisions based only on observations of the improvement in solution quality achieved by each algorithm. We call our approach "low knowledge" since it does not rely on complex prediction models, either of the problem domain or of algorithm behavior. We show that a low-knowledge approach results in a system that achieves significantly better performance than all of the pure algorithms without requiring additional human expertise. Furthermore the low-knowledge approach achieves performance equivalent to a perfect high-knowledge classification approach. [source] The Role of Statistics in the Data Revolution?INTERNATIONAL STATISTICAL REVIEW, Issue 1 2001Jerome H. Friedman Summary The nature of data is rapidly changing. Data sets are becoming increasingly large and complex. Modern methodology for analyzing these new types of data are emerging from the fields of Data Base Managment, Artificial Intelligence, Machine Learning, Pattern Recognition, and Data Visualization. So far Statistics as a field has played a minor role. This paper explores some of the reasons for this, and why statisticians should have an interest in participating in the development of new methods for large and complex data sets. [source] Real Time Foot Drop Correction using Machine Learning and Natural SensorsNEUROMODULATION, Issue 1 2002Morten Hansen MScEE Abstract The objective of this study was to investigate and test a real time system implemented for Functional Electrical Stimulation (FES) assisted foot drop correction, deriving control timing from signals recorded from a peripheral sensory nerve. A hemiplegic participant was attached with a cuff electrode on the sural nerve connected to a telemetry controlled implanted neural amplifier, and a stimulation cuff electrode on the peroneal nerve connected to an implanted stimulator. An input domain was derived from the recorded electroneurogram (ENG) and fed to a detection algorithm based on an Adaptive Logic Network (ALN) for controlling the timing of the peroneal stimulation. The detection system was tested in real time over a period of 392 days, covering a variety of walking tasks. The detection system's ability to detect heel strike and foot lift without errors and to detect the difference between walking and standing proved to be stable for the duration of the study. We conclude that using ALNs and natural sensors provide a stable and accurate control signal for FES foot drop correction. [source] Introduction to Machine Learning and Bioinformatics by MITRA, S., DATTA, S., PERKINS, T., and MICHAILIDIS, G.BIOMETRICS, Issue 1 2009Yulan Liang No abstract is available for this article. [source] Extracting new patterns for cardiovascular disease prognosisEXPERT SYSTEMS, Issue 5 2009Luis Mena Abstract: Cardiovascular diseases constitute one of the main causes of mortality in the world, and machine learning has become a powerful tool for analysing medical data in the last few years. In this paper we present an interdisciplinary work based on an ambulatory blood pressure study and the development of a new classification algorithm named REMED. We focused on the discovery of new patterns for abnormal blood pressure variability as a possible cardiovascular risk factor. We compared our results with other classification algorithms based on Bayesian methods, decision trees, and rule induction techniques. In the comparison, REMED showed similar accuracy to these algorithms but it has the advantage of being superior in its capacity to classify sick people correctly. Therefore, our method could represent an innovative approach that might be useful in medical decision support for cardiovascular disease prognosis. [source] On the Application of Inductive Machine Learning Tools to Geographical AnalysisGEOGRAPHICAL ANALYSIS, Issue 2 2000Mark Gahegan Inductive machine learning tools, such as neural networks and decision trees, offer alternative methods for classification, clustering, and pattern recognition that can, in theory, extend to the complex or "deep" data sets that pervade geography. By contrast, traditional statistical approaches may fail, due to issues of scalability and flexibility. This paper discusses the role of inductive machine learning as it relates to geographical analysis. The discussion presented is not based on comparative results or on mathematical description, but instead focuses on the often subtle ways in which the various inductive learning approaches differ operationally, describing (1) the manner in which the feature space is partitioned or clustered, (2) the search mechanisms employed to identify good solutions, and (3) the different biases that each technique imposes. The consequences arising from these issues, when considering complex geographic feature spaces, are then described in detail. The overall aim is to provide a foundation upon which reliable inductive analysis methods can be constructed, instead of depending on piecemeal or haphazard experimentation with the various operational criteria that inductive learning tools call for. Often, it would appear that these criteria are not well understood by practitioners in the geographic sphere, which can lead to difficulties in configuration and operation, and ultimately to poor performance. [source] Discrete dynamic Bayesian network analysis of fMRI dataHUMAN BRAIN MAPPING, Issue 1 2009John Burge Abstract We examine the efficacy of using discrete Dynamic Bayesian Networks (dDBNs), a data-driven modeling technique employed in machine learning, to identify functional correlations among neuroanatomical regions of interest. Unlike many neuroimaging analysis techniques, this method is not limited by linear and/or Gaussian noise assumptions. It achieves this by modeling the time series of neuroanatomical regions as discrete, as opposed to continuous, random variables with multinomial distributions. We demonstrated this method using an fMRI dataset collected from healthy and demented elderly subjects (Buckner, et al., 2000: J Cogn Neurosci 12:24-34) and identify correlates based on a diagnosis of dementia. The results are validated in three ways. First, the elicited correlates are shown to be robust over leave-one-out cross-validation and, via a Fourier bootstrapping method, that they were not likely due to random chance. Second, the dDBNs identified correlates that would be expected given the experimental paradigm. Third, the dDBN's ability to predict dementia is competitive with two commonly employed machine-learning classifiers: the support vector machine and the Gaussian naïve Bayesian network. We also verify that the dDBN selects correlates based on non-linear criteria. Finally, we provide a brief analysis of the correlates elicited from Buckner et al.'s data that suggests that demented elderly subjects have reduced involvement of entorhinal and occipital cortex and greater involvement of the parietal lobe and amygdala in brain activity compared with healthy elderly (as measured via functional correlations among BOLD measurements). Limitations and extensions to the dDBN method are discussed. Hum Brain Mapp, 2009. © 2007 Wiley-Liss, Inc. [source] End-user access to multiple sources: incorporating knowledge discovery into knowledge managementINTELLIGENT SYSTEMS IN ACCOUNTING, FINANCE & MANAGEMENT, Issue 4 2002Katharina Morik The End-User Access to Multiple Sources,Eams system,integrates given information sources into a knowledge management system. It relates the world of documents with the database world using an ontology. The focus of developing the Eams system is on the acquisition and maintenance of knowledge. Hence, in both worlds, machine learning is applied. In the document world, a learning search engine adapts to user behaviour by analysing the click-through-data. This eases the personalization of selecting appropriate documents for users and does not require further maintenance. In the database world, knowledge discovery in databases (KDD) bridges the gap between the ,ne granularity of relational databases and the actual information needs of users. KDD extracts knowledge from data and, therefore, allows the knowledge management system to make good use of already existing company data,without further acquisition or maintenance. A graphical user interface provides users with a uniform access to document collections on the Internet (Intranet) as well as to relational databases. Since the ontology generates the items in the user interface, a change in the ontology automatically changes the user interface without further efforts. The Eams system has been applied to customer relationship management in the insurance domain. Questions to be answered by the system concern customer acquisition (e.g. direct marketing), customer up- and cross-selling (e.g. which products sell well together), and customer retention (here, which customers are likely to leave the insurance company or ask for a return of a capital life insurance). Documents about other insurance companies and demographic data published on the Internet contribute to the answers, as do the results of data analysis of the company's contracts. Copyright © 2003 John Wiley & Sons, Ltd. [source] Rough reduction in algebra view and information viewINTERNATIONAL JOURNAL OF INTELLIGENT SYSTEMS, Issue 6 2003Guoyin Wang Rough set (RS) is a valid theory to deal with imprecise, uncertain, and vague information. It has been applied successfully since it was developed by Professor Z. Pawlak in 1982 in such fields as machine learning, data mining, intelligent data analyzing, control algorithm acquiring, etc. The greatest advantage of the RS is its great ability to compute the reductions of information systems. Many researchers have done a lot of work in developing efficient algorithms to compute useful reductions of information systems. There also are some researchers working on the relationship between rough entropy and information entropy. They have developed some efficient reduction algorithms based on conditional information entropy. In this article, the relationship of the definitions of rough reduction in algebra view and information view is studied. Some relationships such as inclusion relationship under some conditions and equivalence relationship under some other conditions are presented. The inclusion relationship between the attribute importance defined in algebra view and information view is presented also. Some efficient heuristic reduction algorithms can be developed further using these results. © 2003 Wiley Periodicals, Inc. [source] Machine learning approaches for prediction of linear B-cell epitopes on proteinsJOURNAL OF MOLECULAR RECOGNITION, Issue 3 2006Johannes Söllner Abstract Identification and characterization of antigenic determinants on proteins has received considerable attention utilizing both, experimental as well as computational methods. For computational routines mostly structural as well as physicochemical parameters have been utilized for predicting the antigenic propensity of protein sites. However, the performance of computational routines has been low when compared to experimental alternatives. Here we describe the construction of machine learning based classifiers to enhance the prediction quality for identifying linear B-cell epitopes on proteins. Our approach combines several parameters previously associated with antigenicity, and includes novel parameters based on frequencies of amino acids and amino acid neighborhood propensities. We utilized machine learning algorithms for deriving antigenicity classification functions assigning antigenic propensities to each amino acid of a given protein sequence. We compared the prediction quality of the novel classifiers with respect to established routines for epitope scoring, and tested prediction accuracy on experimental data available for HIV proteins. The major finding is that machine learning classifiers clearly outperform the reference classification systems on the HIV epitope validation set. Copyright © 2006 John Wiley & Sons, Ltd. [source] EFD: A Hybrid Knowledge/Statistical-Based System for the Detection of FraudJOURNAL OF RISK AND INSURANCE, Issue 3 2002John A. Major Electronic Fraud Detection (EFD) assists Investigative Consultants in the Managed Care & Employee Benefits Security Unit of The Travelers Insurance Companies in the detection and preinvestigative analysis of health care provider fraud. The task EFD performs, scanning a large population of health insurance claims in search of likely fraud, has never been done manually. Furthermore, the available database has few positive examples. Thus, neither existing knowledge engineering techniques nor statistical methods are sufficient for designing the identification process. To overcome these problems, EFD uses knowledge discovery techniques on two levels. First, EFD integrates expert knowledge with statistical information assessment to identify cases of unusual provider behavior. The heart of EFD is 27 behavioral heuristics, knowledge-based ways of viewing and measuring provider behavior. Rules operate on them to identify providers whose behavior merits a closer look by the investigative consultants. Second, machine learning is used to develop new rules and improve the identification process. Pilot operations involved analysis of nearly 22,000 providers in six metropolitan areas. The pilot is implemented in SAS Institute's SAS System, AICorp's Knowledge Base Management System, and Borland International's Turbo Prolog. [source] HAADS: A Hebrew Aramaic abbreviation disambiguation systemJOURNAL OF THE AMERICAN SOCIETY FOR INFORMATION SCIENCE AND TECHNOLOGY, Issue 9 2010Yaakov HaCohen-Kerner In many languages abbreviations are very common and are widely used in both written and spoken language. However, they are not always explicitly defined and in many cases they are ambiguous. This research presents a process that attempts to solve the problem of abbreviation ambiguity using modern machine learning (ML) techniques. Various baseline features are explored, including context-related methods and statistical methods. The application domain is Jewish Law documents written in Hebrew and Aramaic, which are known to be rich in ambiguous abbreviations. Two research approaches were implemented and tested: general and individual. Our system applied four common ML methods to find a successful integration of the various baseline features. The best result was achieved by the SVM ML method in the individual research, with 98.07% accuracy. [source] Two-sample Comparison Based on Prediction Error, with Applications to Candidate Gene Association StudiesANNALS OF HUMAN GENETICS, Issue 1 2007K. Yu Summary To take advantage of the increasingly available high-density SNP maps across the genome, various tests that compare multilocus genotypes or estimated haplotypes between cases and controls have been developed for candidate gene association studies. Here we view this two-sample testing problem from the perspective of supervised machine learning and propose a new association test. The approach adopts the flexible and easy-to-understand classification tree model as the learning machine, and uses the estimated prediction error of the resulting prediction rule as the test statistic. This procedure not only provides an association test but also generates a prediction rule that can be useful in understanding the mechanisms underlying complex disease. Under the set-up of a haplotype-based transmission/disequilibrium test (TDT) type of analysis, we find through simulation studies that the proposed procedure has the correct type I error rates and is robust to population stratification. The power of the proposed procedure is sensitive to the chosen prediction error estimator. Among commonly used prediction error estimators, the .632+ estimator results in a test that has the best overall performance. We also find that the test using the .632+ estimator is more powerful than the standard single-point TDT analysis, the Pearson's goodness-of-fit test based on estimated haplotype frequencies, and two haplotype-based global tests implemented in the genetic analysis package FBAT. To illustrate the application of the proposed method in population-based association studies, we use the procedure to study the association between non-Hodgkin lymphoma and the IL10 gene. [source] Unsolved problems in observational astronomy.ASTRONOMISCHE NACHRICHTEN, Issue 6-8 2004Abstract The existence of rapidly slewing robotic telescopes and fast alert distribution via the Internet is revolutionizing our capability to study the physics of fast astrophysical transients. But the salient challenge that optical time domain surveys must conquer is mining the torrent of data to recognize important transients in a scene full of normal variations. Humans simply do not have the attention span, memory, or reaction time required to recognize fast transients and rapidly respond. Autonomous robotic instrumentation with the ability to extract pertinent information from the data stream in real time will therefore be essential for recognizing transients and commanding rapid follow-up observations while the ephemeral behavior is still present. Here we discuss how the development and integration of three technologies: (1) robotic telescope networks; (2) machine learning; and (3) advanced database technology, can enable the construction of smart robotic telescopes, which we loosely call "thinking" telescopes, capable of mining the sky in real time. (© 2004 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim) [source] COGNITIVE AND BEHAVIORAL MODEL ENSEMBLES FOR AUTONOMOUS VIRTUAL CHARACTERSCOMPUTATIONAL INTELLIGENCE, Issue 2 2010Jeffrey S. Whiting Cognitive and behavioral models have become popular methods for creating autonomous self-animating characters. Creating these models present the following challenges: (1) creating a cognitive or behavioral model is a time-intensive and complex process that must be done by an expert programmer and (2) the models are created to solve a specific problem in a given environment and because of their specific nature cannot be easily reused. Combining existing models together would allow an animator, without the need for a programmer, to create new characters in less time and to leverage each model's strengths, resulting in an increase in the character's performance and in the creation of new behaviors and animations. This article provides a framework that can aggregate existing behavioral and cognitive models into an ensemble. An animator has only to rate how appropriately a character performs in a set of scenarios and the system then uses machine learning to determine how the character should act given the current situation. Empirical results from multiple case studies validate the approach. [source] | |||||||||||||||||||