| 000 | 07418nam a22006135i 4500 | ||
|---|---|---|---|
| 001 | 978-3-642-03070-3 | ||
| 003 | DE-He213 | ||
| 005 | 20240423125928.0 | ||
| 007 | cr nn 008mamaa | ||
| 008 | 100301s2009 gw | s |||| 0|eng d | ||
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_a9783642030703 _9978-3-642-03070-3 |
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| 024 | 7 |
_a10.1007/978-3-642-03070-3 _2doi |
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| 050 | 4 | _aQA76.76.C65 | |
| 072 | 7 |
_aUMC _2bicssc |
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_aCOM010000 _2bisacsh |
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_aUMC _2thema |
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_a005.45 _223 |
| 245 | 1 | 0 |
_aMachine Learning and Data Mining in Pattern Recognition _h[electronic resource] : _b6th International Conference, MLDM 2009, Leipzig, Germany, July 23-25, 2009, Proceedings / _cedited by Petra Perner. |
| 250 | _a1st ed. 2009. | ||
| 264 | 1 |
_aBerlin, Heidelberg : _bSpringer Berlin Heidelberg : _bImprint: Springer, _c2009. |
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| 300 |
_aXIV, 824 p. _bonline resource. |
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| 336 |
_atext _btxt _2rdacontent |
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| 337 |
_acomputer _bc _2rdamedia |
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| 338 |
_aonline resource _bcr _2rdacarrier |
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| 347 |
_atext file _bPDF _2rda |
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| 490 | 1 |
_aLecture Notes in Artificial Intelligence, _x2945-9141 ; _v5632 |
|
| 505 | 0 | _aAttribute Discretization and Data Preparation -- Improved Comprehensibility and Reliability of Explanations via Restricted Halfspace Discretization -- Selection of Subsets of Ordered Features in Machine Learning -- Combination of Vector Quantization and Visualization -- Discretization of Target Attributes for Subgroup Discovery -- Preserving Privacy in Time Series Data Classification by Discretization -- Using Resampling Techniques for Better Quality Discretization -- Classification -- A Large Margin Classifier with Additional Features -- Sequential EM for Unsupervised Adaptive Gaussian Mixture Model Based Classifier -- Optimal Double-Kernel Combination for Classification -- Efficient AdaBoost Region Classification -- A Linear Classification Method in a Very High Dimensional Space Using Distributed Representation -- PMCRI: A Parallel Modular Classification Rule Induction Framework -- Dynamic Score Combination: A Supervised and Unsupervised Score Combination Method -- ODDboost: Incorporating Posterior Estimates into AdaBoost -- Ensemble Classifier Learning -- Ensemble Learning: A Study on Different Variants of the Dynamic Selection Approach -- Relevance and Redundancy Analysis for Ensemble Classifiers -- Drift-Aware Ensemble Regression -- Concept Drifting Detection on Noisy Streaming Data in Random Ensemble Decision Trees -- Association Rules and Pattern Mining -- Mining Multiple Level Non-redundant Association Rules through Two-Fold Pruning of Redundancies -- Pattern Mining with Natural Language Processing: An Exploratory Approach -- Is the Distance Compression Effect Overstated? Some Theory and Experimentation -- Support Vector Machines -- Fast Local Support Vector Machines for Large Datasets -- The Effect of Domain Knowledge on Rule Extraction from Support Vector Machines -- Towards B-Coloring of SOM -- Clustering -- CSBIterKmeans: A New Clustering Algorithm Based on Quantitative Assessment of the Clustering Quality -- Agent-Based Non-distributed and Distributed Clustering -- An Evidence Accumulation Approach to Constrained Clustering Combination -- Fast Spectral Clustering with Random Projection and Sampling -- How Much True Structure Has Been Discovered? -- Efficient Clustering of Web-Derived Data Sets -- A Probabilistic Approach for Constrained Clustering with Topological Map -- Novelty and Outlier Detection -- Relational Frequent Patterns Mining for Novelty Detection from Data Streams -- A Comparative Study of Outlier Detection Algorithms -- Outlier Detection with Explanation Facility -- Learning -- Concept Learning from (Very) Ambiguous Examples -- Finding Top-N Pseudo Formal Concepts with Core Intents -- On Fixed Convex Combinations of No-Regret Learners -- An Improved Tabu Search (ITS) Algorithm Based on Open Cover Theory for Global Extremums -- The Needles-in-Haystack Problem -- Data Mining on Multimedia Data -- An Evidence-Driven Probabilistic Inference Framework for Semantic Image Understanding -- Detection of Masses in Mammographic Images Using Simpson’s Diversity Index in Circular Regions and SVM -- Mining Lung Shape from X-Ray Images -- A Wavelet-Based Method for Detecting Seismic Anomalies in Remote Sensing Satellite Data -- Spectrum Steganalysis of WAV Audio Streams -- Audio-Based Emotion Recognition in Judicial Domain: A Multilayer Support Vector Machines Approach -- Learning with a Quadruped Chopstick Robot -- Dissimilarity Based Vector Space Embedding of Graphs Using Prototype Reduction Schemes -- Text Mining -- Using Graph-Kernels to Represent Semantic Information in Text Classification -- A General Framework of Feature Selection for Text Categorization -- New SemanticSimilarity Based Model for Text Clustering Using Extended Gloss Overlaps -- Aspects of Data Mining -- Learning Betting Tips from Users’ Bet Selections -- An Approach to Web-Scale Named-Entity Disambiguation -- A General Learning Method for Automatic Title Extraction from HTML Pages -- Regional Pattern Discovery in Geo-referenced Datasets Using PCA -- Memory-Based Modeling of Seasonality for Prediction of Climatic Time Series -- A Neural Approach for SME’s Credit Risk Analysis in Turkey -- Assisting Data Mining through Automated Planning -- Predictions with Confidence in Applications -- Data Mining in Medicine -- Aligning Bayesian Network Classifiers with Medical Contexts -- Assessing the Eligibility of Kidney Transplant Donors -- Lung Nodules Classification in CT Images Using Simpson’s Index, Geometrical Measures and One-Class SVM. | |
| 520 | _aThis book constitutes the refereed proceedings of the 6th International Conference on Machine Learning and Data Mining in Pattern Recognition, MLDM 2009, held in Leipzig, Germany, in July 2009. The 63 revised full papers presented were carefully reviewed and selected from 205 submissions. The papers are organized in topical sections on attribute discretization and data preparation; classification; ensemble classifier learning; associate rules and pattern minig; support vector machines; clustering; novelty and outlier detection; learning; data mining and multimedia data; text mining; aspects of data mining; as well as data mining in medicine. | ||
| 650 | 0 | _aCompilers (Computer programs). | |
| 650 | 0 | _aData mining. | |
| 650 | 0 | _aApplication software. | |
| 650 | 0 | _aArtificial intelligence. | |
| 650 | 0 | _aMachine theory. | |
| 650 | 0 | _aDatabase management. | |
| 650 | 1 | 4 | _aCompilers and Interpreters. |
| 650 | 2 | 4 | _aData Mining and Knowledge Discovery. |
| 650 | 2 | 4 | _aComputer and Information Systems Applications. |
| 650 | 2 | 4 | _aArtificial Intelligence. |
| 650 | 2 | 4 | _aFormal Languages and Automata Theory. |
| 650 | 2 | 4 | _aDatabase Management. |
| 700 | 1 |
_aPerner, Petra. _eeditor. _4edt _4http://id.loc.gov/vocabulary/relators/edt |
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| 710 | 2 | _aSpringerLink (Online service) | |
| 773 | 0 | _tSpringer Nature eBook | |
| 776 | 0 | 8 |
_iPrinted edition: _z9783642030697 |
| 776 | 0 | 8 |
_iPrinted edition: _z9783642030710 |
| 830 | 0 |
_aLecture Notes in Artificial Intelligence, _x2945-9141 ; _v5632 |
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| 856 | 4 | 0 | _uhttps://doi.org/10.1007/978-3-642-03070-3 |
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