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Evolutionary Support Vector machines for Diabetes Mellitus Diagnosis

Evolutionary Support Vector Machines for Diabetes Mellitus D...

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international IEEE conference on Intelligent Systems, IS

作者： Ruxandra Stoean Catalin Stoean Mike Preuss Elia El-Darzi D. Dumitrescu Department of Computer Science Faculty of Mathematics and Computer Science University of Craiova Craiova Romania Department of Computer Science University of Dortmund Germany Department of Computer Science University of Westminster London UK Department of Computer Science Faculty of Mathematics and Computer Science Babes Bolyai University Cluj-Napoca Romania

The aim of this paper is to validate the new paradigm of evolutionary support vector machines (ESVMs) for binary classification also through an application to a real-world problem, i.e. the diagnosis of diabetes mellitus. ESVMs were developed through hybridization between the strong learning paradigm of support vector machines (SVMs) and the optimization power of evolutionary computation. Hybridization is achieved at the level of solving the constrained optimization problem within the SVMs, which is a difficult task to perform in its standard manner. ESVMs have been so far applied to the binary classification of two-dimensional points. In this paper, experiments are conducted on the benchmark problem concerning diabetes of the UCI repository of machine learning data sets. Obtained results proved the correctness and promise of the new hybridized learning technique and demonstrated its ability to solve any case of binary standard classification

关键词： Support vector machines Diabetes computer science Support vector machine classification Evolutionary computation machine learning Mathematics Lagrangian functions Intelligent systems machine intelligence

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Optimizers with learning distributions in simulation models: An application to all-pass dispersion compensator of optic filters

Optimizers with learning distributions in simulation models:...

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2005 3rd international Industrial Simulation conference, ISC 2005

作者： Ozden, Mufit Cheng, Chi-Hao Department of Computer Science and Systems Analysis Miami University Oxford OH 45056 United States Department of Electrical and Computer Engineering Miami University Oxford OH 45056 United States

ISBN: (纸本)907738118X

We apply the multiple optimizers with the learning- distributions to continuous decisions in a simulation model as integral decision-making components. In this machine learning technique, the optimizers are given performance evaluations estimated for their decisions during short simulation runs. With the feedback, the optimizers modify their decision-making distributions after the decisions with good performance judged against a threshold value and they generate new decisions for the next simulation run. With the gradually advancing threshold value, the optimizers learn the structural components of the optimal solution. Because of the probabilistic nature of their decision-making processes, the optimizers have proved to be very robust in the random environments with the noisy responses. The development of this machine learning technique and the convergence properties have been delineated in an earlier paper in detail. In this paper, we present an engineering application to a fiber optic communication problem after brief explanation of the optimizers. © 2005 EUROSIS-ETI.

关键词： Decision making

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Applying machine learning using Case-Based Reasoning (CBR) and Rule-Based Reasoning (RBR) approaches to object-oriented application framework documentation

Applying machine learning using Case-Based Reasoning (CBR) a...

引用

3rd international conference on Information Technology and applications, ICITA 2005

作者： Jani, Hajar Mat Peck, Lee Sai College of Information Technology Universiti Tenaga Nasional Km. 7 Jalan Kajang-Puchong 43009 Kajang Selangor Darul Ehsan Malaysia Department of Software Engineering Faculty of Computer Science and Information Technology University Malaya 50603 Kuala Lumpur Malaysia

ISBN: (纸本)0769523161

Several challenges and problems of developing, using and maintaining object-oriented application frameworks have been identified. It was discovered that companies attempting to build or use large-scale reusable framework often fail unless they recognize and resolve challenges such as development effort, learning curve, integratability, maintainability, validation, defect removal, efficiency, and lack of standards. Framework documentation plays a major role in facing the above challenges. It directly affects the learning curve, maintainability, and defect removal aspects of the application frameworks. We have studied various documenting approaches and concluded that the current approaches are not very effective in overcoming the above challenges, especially on the efficiency problem. So, in this paper we are going to apply machine learning using case-based reasoning (CBR) and rule-based reasoning (RBR) to framework documentation. We will come up with a documentation architecture that combines both techniques in order to come up with improved framework documentation. © 2005 IEEE.

关键词： learning systems

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learning meta-descriptions of the FOAF network

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3rd international Semantic Web conference, ISWC 2004

作者： Grimnes, Gunnar Aastrand Edwards, Pete Preece, Alun Computing Science Dept King’s College University of Aberdeen AB24 3UE United Kingdom

ISBN: (纸本)3540237984

We argue that in a distributed context, such as the Semantic Web, ontology engineers and data creators often cannot control (or even imagine) the possible uses their data or ontologies might have. Therefore ontologies are unlikely to identify every useful or interesting classification possible in a problem domain, for example these might be of a personalised nature and only appropriate for a certain user in a certain context, or they might be of a different granularity than the initial scope of the *** argue that machine learning techniques will be essential within the Semantic Web context to allow these unspecified classifications to be identified. In this paper we explore the application of machine learning methods toFOAF, highlighting the challenges posed by the characteristics of such data. Specifically, we use clustering to identify classes of people and inductive logic programming (ILP) to learn descriptions of these *** argue that these descriptions constitute re-usable, first class knowledge that is neither explicitly stated nor deducible from the input data. These new descriptions can be represented as simple OWL class restrictions or more sophisticated descriptions using SWRL. These are then suitable either for incorporation into future versions of ontologies or for on-the-fly use for personalisation tasks. © Springer-Verlag Berlin Heidelberg 2004.

关键词： Ontology

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CLOVES: A virtual world builder for constructing virtual environments for science inquiry learning

CLOVES: A virtual world builder for constructing virtual env...

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3rd international conference on Entertainment Computing, ICEC 2004

作者： Cho, Yongjoo Park, Kyoung Shin Moher, Thomas Johnson, Andrew E. Chang, Juno Whang, Min Cheol Lim, Joa Sang Rhee, Dae-Woong Park, Kang Ryoung Park, Hung Kook Sangmyung University 7 Hongji-dong Jongno-gu Seoul Korea Republic of ICU Digital Media Lab 517-10 Dogok-dong Gangnam-gu Seoul Korea Republic of University of Illinois at Chicago 851 S. Morgan ChicagoIL60607 United States

ISBN: (纸本)9783540286431

This paper presents the motivation, design, and a preliminary evaluation of a virtual world builder, CLOVES. CLOVES is designed to support rapid construction of data-rich virtual environments and instruments for young children’s science inquiry learning. It provides a layered programming interface such as a visual design environment, scripting layer, and low-level application programming interface targeting for multiple levels of programming expertise. It is also intended to be a collaborative medium among interdisciplinary domain experts such as educators, modelers and software developers. A case study was conducted to evaluate the capabilities and effectiveness of CLOVES. The results showed that designers actively participated in decision making at every stage of the design process and shared knowledge among one another. © IFIP international Federation for Information Processing 2004.

关键词： Virtual reality

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3rd international conference on machine learning and Data Mining in Pattern Recognition, MLDM 2003

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3rd international conference on machine learning and Data Mining, MLDM 2003

ISBN: (纸本)3540405046

The proceedings contain 37 papers. The special focus in this conference is on Decision Trees, Clustering and Its application. The topics include: Introspective learning to build case-based reasoning CBR knowledge containers;graph-based tools for data mining and machine learning;simplification methods for model trees with regression and splitting nodes;learning multi-label alternating decision trees from texts and data;a discretization method of continuous attributes with guaranteed resistance to noise;on the size of a classification tree;a comparative analysis of clustering algorithms applied to load profiling;similarity-based clustering of sequences using hidden markov models;a fast parallel optimization for training support vector machine;a ROC-based reject rule for support vector machines;remembering similitude terms in CBR;authoring cases from free-text maintenance data;classification boundary approximation by using combination of training steps for real-time image segmentation;simple mimetic classifiers;novel mixtures based on the dirichlet distribution;estimating a quality of decision function by empirical risk;efficient locally linear embeddings of imperfect manifolds;dissimilarity representation of images for relevance feedback in content-based image retrieval;a rule-based scheme for filtering examples from majority class in an imbalanced training set;coevolutionary feature learning for object recognition;generalization of pattern-growth methods for sequential pattern mining with gap constraints;discover motifs in multi-dimensional time-series using the principal component analysis and the MDL principle and optimizing financial portfolios from the perspective of mining temporal structures of stock returns.

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Stock price and index forecasting by arbitrage pricing theory-based Gaussian TFA learning 3rd

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3rd international conference on Intelligent Data Engineering and Automated learning, IDEAL 2002

作者： Chiu, Kai Chun Xu, Lei Department of Computer Science and Engineering The Chinese University of Hong Kong Shatin N.T. Hong Kong Hong Kong

ISBN: (纸本)9783540440253

Viewed as a promising application of neural networks, financial time series forecasting was studied in the literature of neural nets and machine learning. The recently developed Temporal Factor Analysis (TFA) model mainly targeted at further study of the Arbitrage Pricing Theory (APT) is found to have potential application in the prediction of stock price and index. In this paper, we aim to illustrate the superiority of using the APT-based Gaussian TFA model as compared to three conventional approaches which are not financial model-based. © Springer-Verlag Berlin Heidelberg 2002.

关键词： Forecasting

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Graph-based induction for general graph structured data and its application to chemical compound data 3rd

引用

3rd international conference on Discovery Science, DS 2000

作者： Matsuda, Takashi Horiuchi, Tadashi Motoda, Hiroshi Washio, Takashi I.S.I.R Osaka University 8-1 Mihogaoka IbarakiOsaka567-0047 Japan

ISBN: (纸本)9783540413523

Most of the relations are represented by a graph structure, e.g., chemical bonding, Web browsing record, DNA sequence, Inference pattern (program trace), to name a few. Thus, efficiently finding characteristic substructures in a graph will be a useful technique in many important KDD/ML applications. However, graph pattern matching is a hard problem. We propose a machine learning technique called Graph-Based Induction (GBI) that efficiently extracts typical patterns from graph data in an approximate manner by stepwise pair expansion (pair-wise chunking). It can handle general graph structured data, i.e., directed/undirected, colored/uncolored graphs with/without (self) loop and with colored/uncolored links. We show that its time complexity is almost linear with the size of graph. We, further, show that GBI can effectively be applied to the extraction of typical patterns from chemical compound data from which to generate classification rules, and that GBI also works as a feature construction component for other machine learning tools. © Springer-Verlag Berlin Heidelberg 2000.

关键词： Artificial intelligence

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An approach to learning software based on Student modelling 3rd

引用

3rd international conference on computer Aided learning and Instruction in Science and Engineering, CALISCE 1996

作者： Gavrilova, Tatjana Averbukh, Elena Voinov, Alexander Institute of High Performance Computing and Databases Pbox 1146 St. Petersburg194291 Russia Laboratory for Systems Engineering and Human-Machine Systems University of Kassel FB 15 KasselD-34109 Germany

ISBN: (纸本)3540614915

A hypermedia tutorial describing an approach to building adaptive courseware by Student Modeling is presented. This tutorial combines both the hypertext traversal techniques and the demo/training animation features, which enable one to practice the learned material directly during the session. The key principles of both Student and Interface Modeling and their interaction, together with the issues of Scenario Control for the adaptive tutorial generation are thoroughly explained in the presented application and briefly sketched in the current paper. © Springer-Verlag Berlin Heidelberg 1996.

关键词： Students

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application of machine learning to the maintenance of knowledge-based performance 90

Application of machine learning to the maintenance of knowle...

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Proceedings of the 3rd international conference on Industrial and engineering applications of artificial intelligence and expert systems - Volume 2

作者： Lawrence B. Holder University of Illinois Department of Computer Science 405 North Mathews Urbana IL

ISBN: (纸本)9780897913720

Integration of machine learning methods into knowledge-based systems requires greater control over the application of the learning methods. Recent research in machine learning has shown that isolated and unconstrained application of learning methods can eventually degrade performance. This paper presents an approach called performance-driven knowledge transformation for controlling the application of learning methods. The primary guidance for the control is performance of the knowledge base. The approach is implemented in the PEAK system. Two experiments with PEAK illustrate how the knowledge base is transformed using different learning methods to maintain performance goals. Results demonstrate the ability of performance-driven knowledge transformation to control the application of learning methods and maintain knowledge base performance.

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