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检索条件"任意字段=4th International Conference on Machine Learning and Data Mining in Pattern Recognition, MLDM 2005"

共 135 条记录，以下是121-130 订阅

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Proceedings: ICMLA 2005 Fourth international conference on machine learning and Applications

Proceedings: ICMLA 2005 Fourth International Conference on M...

引用

ICMLA 2005: 4th international conference on machine learning and Applications

ISBN: (纸本)0769524958

the proceedings contain 60 papers. the topics discussed include: predicting software suitability using a Bayesian belief network;parallel algorithm for control chart pattern recognition;data-centric automated data mining;a Bayesian kernel for the prediction of neuron properties from binary gene profiles;new filter-based feature selection criteria for identifying differentially expressed genes;a new clustering algorithm using message passing and its applications in analyzing microarray data;iterative weighting of phylogenetic profiles increases classification accuracy;integrating knowledge-driven and data-driven approaches for the derivation of clinical prediction rules;sparse classifiers for automated heart wall motion abnormality detection;segmenting brain tumors using alignment-based features;and the application of machine learning techniques to the prediction of erectile dysfunction.

关键词： learning systems

来源：评论

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Determining regularization parameters for derivative free neural learning

引用

4th international conference on machine learning and data mining in pattern recognition

作者： Ghosh, R Ghosh, M Yearwood, J Bagirov, A Univ Ballarat Sch Informat Technol & Math Sci Ballarat Vic 3353 Australia

ISBN: (纸本)3540269231

Derivative free optimization methods have recently gained a lot of attractions for neural learning. the curse of dimensionality for the neural learning problem makes local optimization methods very attractive;however the error surface contains many local minima. Discrete gradient method is a special case of derivative free methods based on bundle methods and has the ability to jump over many local minima. there are two types of problems that are associated with this when local optimization methods are used for neural learning. the first type of problems is initial sensitivity dependence problem - that is commonly solved by using a hybrid model. Our early research has shown that discrete gradient method combining with other global methods such as evolutionary algorithm makes them even more attractive. these types of hybrid models have been studied by other researchers also. Another less mentioned problem is the problem of large weight values for the synaptic connections of the network. Large synaptic weight values often lead to the problem of paralysis and convergence problem especially when a hybrid model is used for fine tuning the learning task. In this paper we study and analyse the effect of different regularization parameters for our objective function to restrict the weight values without compromising the classification accuracy.

关键词： learning systems

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Kernel based intrusion detection system 05

Kernel based intrusion detection system

引用

4th Annual ACIS international conference on Computer and Information Science, ICIS 2005

作者： Kim, Byung-Joo Kim, Il-Kon Dept. of Info. and Comm. Youngsan University Yangsan Korea Republic of Dept. of Computer Science Kyungpook National University Daegu Korea Republic of

ISBN: (纸本)0769522963

Recently applying artificial intelligence, machine learning and data mining techniques to intrusion detection system are increasing. But most of researches are focused on improving the performance of classifier. Selecting important features from input data lead to a simplification of the problem, faster and more accurate detection rates. thus selecting important features is an important issue in intrusion detection. Another issue in intrusion detection is that most of the intrusion detection systems are performed by off-line and it is not proper method for realtime intrusion detection system. In this paper, we develop the realtime intrusion detection system which combining on-line feature extraction method with Least Squares Support Vector machine classifier. Applying proposed system to KDD CUP 99 data, experimental results show that it have remarkable performance compared to off-line intrusion detection system. © 2005 IEEE.

关键词： pattern recognition systems

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A bayesian framework for regularized SVM parameter estimation

A bayesian framework for regularized SVM parameter estimatio...

引用

4th IEEE international conference on data mining

作者： Gregor, J Liu, ZQ Univ Tennessee Dept Comp Sci Knoxville TN 37996 USA

ISBN: (纸本)0769521428

the support vector machine (SVM) is considered here in the context of pattern classification, the emphasis is on the soft margin classifier which uses regularization to handle non-separable learning samples. We present an SVM parameter estimation algorithm that first identifies a subset of,the learning samples that we call the support set and then determines not only the weights of the classifier but, also the hyperparameter that controls the influence of the regularizing penalty term, on basis thereof. We provide numerical results using several data sets from the public domain.

关键词： pattern recognition

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Improving the reliability of decision tree and naive Bayes learners

Improving the reliability of decision tree and naive Bayes l...

引用

4th IEEE international conference on data mining

作者： Lindsay, D Cox, S Univ London Royal Holloway Comp Learning Res Ctr Egham TW20 0EX Surrey England

ISBN: (纸本)0769521428

the C4.5 Decision Tree and Naive Bayes learners are known to produce unreliable probability forecasts. We have used simple Binning [11] and Laplace Transform [2] techniques to improve the reliability of these learners and compare their effectiveness with that of the newly developed Venn Probability machine (VPM) meta-learner [9]. We assess improvements in reliability using loss functions, Receiver Operator Characteristic (ROC) curves and Empirical Reliability Curves (ERC). the VPM outperforms the simple techniques to improve reliability, although at the cost of increased computational intensity and slight increase in error rate. these trade-offs are discussed.

关键词： pattern recognition

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the rough set database system: An overview 4th

引用

4th international conference on Rough Sets and Current Trends in Computing

作者： Suraj, Z Grochowalski, P Univ Informat Technol & Management Chair Comp Sci Fdn Rzeszow Poland Rzeszow Univ Math Inst Rzeszow Poland

ISBN: (纸本)3540221174

the paper describes the "Rough Sets database System" (called in short the RSDS system) for the creation of bibliography on rough sets and their applications. this database is the most comprehensive online rough sets bibliography and accessible under the following web-site address: http://*** the service has been developed in order to facilitate the creation of rough sets bibliography, for various types of publications. At the moment the bibliography contains over 1400 entries from more than 450 authors. It is possible to create the bibliography in HTML or BibTeX format. In order to broaden the service contents it is possible to append new data using specially dedicated form. After appending data online the database is updated automatically. If one prefers sending a data file to the database administrator, please be aware that the database is updated once a month. In the current version of the RSDS system, there is the possibility for appending to each publication an abstract and keywords. As a natural consequence of this improvement there exists a possibility for searching a publication by keywords.

关键词： rough sets fuzzy systems neural networks evolutionary computing data mining knowledge discovery pattern recognition machine learning database systems

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Proof running two state-of-the-art pattern recognition techniques in the field of direct marketing 4

Proof running two state-of-the-art pattern recognition techn...

引用

4th international conference on Enterprise Information Systems, ICEIS 2002

作者： Viaene, Stijn Baesens, Bart Dedene, Guido Vanthienen, Jan Van Den Poel, Dirk Department of Applied Economic Sciences K.U. Leuven Naamsestraat 69 LeuvenB-3000 Belgium Department of Marketing Ghent University Hoveniersberg 24 GhentB-9000 Belgium

ISBN: (纸本)9729805067

In this paper, we synthesize the main findings of three repeat purchase modelling case studies using real-life direct marketing data. Historically, direct marketing - more recently, targeted web marketing - has been one of the most popular domains for the exploration of the feasibility and the viable use of novel business intelligence techniques. Many a data mining technique has been field tested in the direct marketing domain. this can be explained by the (relatively) low-cost availability of recency, frequency, monetary (RFM) and several other customer relationship data, the (relatively) well-developed understanding of the task and the domain, the clearly identifiable costs and benefits, and because the results can often be readily applied to obtain a high return on investment. the purchase incidence modelling cases reported on in this paper were in the first place undertaken to trial run state-of-the-art supervised Bayesian learning multilayer perceptron (MLP) and least squares support vector machine (LS-SVM) classifiers. For each of the cases, we also aimed at exploring the explanatory power (relevance) of the available RFM and other customer relationship related variable operationalizations for predicting purchase incidence in the context of direct marketing.

关键词： Sales

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machine learning and data mining in pattern recognition 1

引用

丛书名： Lecture Notes in Computer Science

1000年

作者： Petra Perner

ISBN: (数字)9783642315374

ISBN: (纸本)9783642315367

this book constitutes the refereed proceedings of the 8th international conference, mldm 2012, held in Berlin, Germany in July 2012. the 51 revised full papers presented were carefully reviewed and selected from 212 submissions. the topics range from theoretical topics for classification, clustering, association rule and pattern mining to specific data mining methods for the different multimedia data types such as image mining, text mining, video mining and web mining.

关键词： Artificial Intelligence Mathematical Logic and Formal Languages database Management data mining and Knowledge Discovery pattern recognition Image Processing and Computer Vision

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machine learning for Networking 1

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丛书名： Lecture Notes in Computer Science

1000年

作者： Éric Renault Selma Boumerdassi Paul Mühlethaler

ISBN: (数字)9783030989781

ISBN: (纸本)9783030989774

this book constitutes the thoroughly refereed proceedings of the 4th international conference on machine learning for Networking, MLN 2021, held in Paris, France, in December 2021. the 10 revised full papers included in the volume were carefully reviewed and selected from 30 submissions. they present and discuss new trends in in deep and reinforcement learning, pattern recognition and classification for networks, machine learning for network slicing optimization, 5G systems, user behavior prediction, multimedia, IoT, security and protection, optimization and new innovative machine learning methods, performance analysis of machine learning algorithms, experimental evaluations of machine learning, data mining in heterogeneous networks, distributed and decentralized machine learning algorithms, intelligent cloud-support communications, resource allocation, energy-aware communications, software-defined networks, cooperative networks, positioning and navigation systems, wireless communications, wireless sensor networks, and underwater sensor networks.

关键词： data mining and Knowledge Discovery Computer Communication Networks Computer Applications

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Artificial Intelligence and Computational Intelligence 1

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丛书名： Lecture Notes in Computer Science

1000年

作者： Jingsheng Lei Fu Lee Wang Hepu Deng Duoqian Miao

ISBN: (数字)9783642334788

ISBN: (纸本)9783642334771

this volume proceedings contains revised selected papers from the 4th international conference on Artificial Intelligence and Computational Intelligence, AICI 2012, held in Chengdu, China, in October 2012. the total of 163 high-quality papers presented were carefully reviewed and selected from 724 submissions. the papers are organized into topical sections on applications of artificial intelligence, applications of computational intelligence, data mining and knowledge discovery, evolution strategy, expert and decision support systems, fuzzy computation, information security, intelligent control, intelligent image processing, intelligent information fusion, intelligent signal processing, machine learning, neural computation, neural networks, particle swarm optimization, and pattern recognition.

关键词： Artificial Intelligence pattern recognition Image Processing and Computer Vision data mining and Knowledge Discovery Computation by Abstract Devices Algorithm Analysis and Problem Complexity

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