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6. MLDM 2009: Leipzig, Germany
- Petra Perner:
Machine Learning and Data Mining in Pattern Recognition, 6th International Conference, MLDM 2009, Leipzig, Germany, July 23-25, 2009. Proceedings. Lecture Notes in Computer Science 5632, Springer 2009, ISBN 978-3-642-03069-7
Attribute Discretization and Data Preparation
- Klaus Truemper:
Improved Comprehensibility and Reliability of Explanations via Restricted Halfspace Discretization. 1-15 - Oleg Seredin
, Andrey Kopylov
, Vadim Mottl:
Selection of Subsets of Ordered Features in Machine Learning. 16-28 - Olga Kurasova
, Alma Molyte:
Combination of Vector Quantization and Visualization. 29-43 - Katherine Moreland, Klaus Truemper:
Discretization of Target Attributes for Subgroup Discovery. 44-52 - Ye Zhu, Yongjian Fu, Huirong Fu:
Preserving Privacy in Time Series Data Classification by Discretization. 53-67 - Taimur Qureshi, Djamel A. Zighed:
Using Resampling Techniques for Better Quality Discretization. 68-81
Classification
- Xinwang Liu
, Jianping Yin, En Zhu, Guomin Zhang, Yubin Zhan, Miaomiao Li:
A Large Margin Classifier with Additional Features. 82-95 - Bashar Awwad Shiekh Hasan, John Q. Gan
:
Sequential EM for Unsupervised Adaptive Gaussian Mixture Model Based Classifier. 96-106 - Feng Wang, Hongbin Zhang:
Optimal Double-Kernel Combination for Classification. 107-122 - Matthijs Moed, Evgueni N. Smirnov:
Efficient AdaBoost Region Classification. 123-136 - Takao Kobayashi, Ikuko Shimizu
:
A Linear Classification Method in a Very High Dimensional Space Using Distributed Representation. 137-147 - Frederic T. Stahl, Max A. Bramer, Mo Adda:
PMCRI: A Parallel Modular Classification Rule Induction Framework. 148-162 - Roberto Tronci
, Giorgio Giacinto
, Fabio Roli
:
Dynamic Score Combination: A Supervised and Unsupervised Score Combination Method. 163-177 - Olga Barinova, Dmitry P. Vetrov:
ODDboost: Incorporating Posterior Estimates into AdaBoost. 178-190
Ensemble Classifier Learning
- João Mendes-Moreira
, Alípio Mário Jorge
, Carlos Soares
, Jorge Freire de Sousa
:
Ensemble Learning: A Study on Different Variants of the Dynamic Selection Approach. 191-205 - Rakkrit Duangsoithong
, Terry Windeatt
:
Relevance and Redundancy Analysis for Ensemble Classifiers. 206-220 - Frank Rosenthal, Peter Benjamin Volk, Martin Hahmann, Dirk Habich, Wolfgang Lehner:
Drift-Aware Ensemble Regression. 221-235 - Pei-Pei Li, Xuegang Hu, Qianhui Liang, Yunjun Gao:
Concept Drifting Detection on Noisy Streaming Data in Random Ensemble Decision Trees. 236-250
Association Rules and Pattern Mining
- Corrado Loglisci
, Donato Malerba
:
Mining Multiple Level Non-redundant Association Rules through Two-Fold Pruning of Redundancies. 251-265 - Ana Cristina Mendes, Cláudia Antunes
:
Pattern Mining with Natural Language Processing: An Exploratory Approach. 266-279 - Stephen L. France, J. Douglas Carroll:
Is the Distance Compression Effect Overstated? Some Theory and Experimentation. 280-294
Support Vector Machines
- Nicola Segata
, Enrico Blanzieri
:
Fast Local Support Vector Machines for Large Datasets. 295-310 - Nahla H. Barakat
, Andrew P. Bradley
:
The Effect of Domain Knowledge on Rule Extraction from Support Vector Machines. 311-321 - Haytham Elghazel, Khalid Benabdeslem:
Towards B-Coloring of SOM. 322-336
Clustering
- Tarek Smaoui, Sascha Müller, Christian Müller-Schloer:
CSBIterKmeans: A New Clustering Algorithm Based on Quantitative Assessment of the Clustering Quality. 337-346 - Ireneusz Czarnowski
, Piotr Jedrzejowicz:
Agent-Based Non-distributed and Distributed Clustering. 347-360 - Daniel Duarte Abdala, Xiaoyi Jiang
:
An Evidence Accumulation Approach to Constrained Clustering Combination. 361-371 - Tomoya Sakai, Atsushi Imiya:
Fast Spectral Clustering with Random Projection and Sampling. 372-384 - Frank Höppner:
How Much True Structure Has Been Discovered? 385-397 - Luís Sarmento, Alexander Kehlenbeck, Eugénio C. Oliveira
, Lyle H. Ungar:
Efficient Clustering of Web-Derived Data Sets. 398-412 - Khalid Benabdeslem, Jihène Snoussi:
A Probabilistic Approach for Constrained Clustering with Topological Map. 413-426
Novelty and Outlier Detection
- Michelangelo Ceci
, Annalisa Appice
, Corrado Loglisci
, Costantina Caruso
, Fabio Fumarola, Carmine Valente, Donato Malerba
:
Relational Frequent Patterns Mining for Novelty Detection from Data Streams. 427-439 - Charlie Isaksson, Margaret H. Dunham:
A Comparative Study of Outlier Detection Algorithms. 440-453 - Manuel Mejía-Lavalle, Atlántida Sánchez Vivar:
Outlier Detection with Explanation Facility. 454-464
Learning
- Dominique Bouthinon, Henry Soldano, Véronique Ventos:
Concept Learning from (Very) Ambiguous Examples. 465-478 - Yoshiaki Okubo, Makoto Haraguchi:
Finding Top-N Pseudo Formal Concepts with Core Intents. 479-493 - Jan-P. Calliess:
On Fixed Convex Combinations of No-Regret Learners. 494-504 - Kemal Yüksek, Serhat Cakaloglu:
An Improved Tabu Search (ITS) Algorithm Based on Open Cover Theory for Global Extremums. 505-515 - Katherine Moreland, Klaus Truemper:
The Needles-in-Haystack Problem. 516-524
Data Mining on Multimedia Data
- Spiros Nikolopoulos, Georgios Th. Papadopoulos, Ioannis Kompatsiaris, Ioannis Patras:
An Evidence-Driven Probabilistic Inference Framework for Semantic Image Understanding. 525-539 - André Pereira Nunes, Aristófanes Corrêa Silva
, Anselmo Cardoso de Paiva
:
Detection of Masses in Mammographic Images Using Simpson's Diversity Index in Circular Regions and SVM. 540-553 - Vassili Kovalev
, Aliaksandr Prus, Pavel Vankevich:
Mining Lung Shape from X-Ray Images. 554-568 - Pan Xiong
, Yaxin Bi, Xuhui Shen:
A Wavelet-Based Method for Detecting Seismic Anomalies in Remote Sensing Satellite Data. 569-581 - Qingzhong Liu
, Andrew H. Sung, Mengyu Qiao:
Spectrum Steganalysis of WAV Audio Streams. 582-593 - Elisabetta Fersini
, Enza Messina
, Gaia Arosio, Francesco Archetti:
Audio-Based Emotion Recognition in Judicial Domain: A Multilayer Support Vector Machines Approach. 594-602 - Wei-Chung Lee, Jong-Chen Chen, Shou-zhe Wu, Kuo-Ming Lin:
Learning with a Quadruped Chopstick Robot. 603-616 - Kaspar Riesen
, Horst Bunke:
Dissimilarity Based Vector Space Embedding of Graphs Using Prototype Reduction Schemes. 617-631
Text Mining
- Teresa Gonçalves
, Paulo Quaresma
:
Using Graph-Kernels to Represent Semantic Information in Text Classification. 632-646 - Hongfang Jing, Bin Wang, Yahui Yang, Yan Xu:
A General Framework of Feature Selection for Text Categorization. 647-662 - Walaa K. Gad
, Mohamed S. Kamel
:
New Semantic Similarity Based Model for Text Clustering Using Extended Gloss Overlaps. 663-677
Aspects of Data Mining
- Erik Strumbelj, Marko Robnik-Sikonja, Igor Kononenko:
Learning Betting Tips from Users' Bet Selections. 678-688 - Luís Sarmento, Alexander Kehlenbeck, Eugénio C. Oliveira
, Lyle H. Ungar:
An Approach to Web-Scale Named-Entity Disambiguation. 689-703 - Sahar Changuel, Nicolas Labroche
, Bernadette Bouchon-Meunier:
A General Learning Method for Automatic Title Extraction from HTML Pages. 704-718 - Oner Ulvi Celepcikay, Christoph F. Eick, Carlos Ordonez:
Regional Pattern Discovery in Geo-referenced Datasets Using PCA. 719-733 - Daniel Nikovski, Ganesan Ramachandran:
Memory-Based Modeling of Seasonality for Prediction of Climatic Time Series. 734-748 - Gülnur Derelioglu, Fikret S. Gürgen, Nesrin Okay:
A Neural Approach for SME's Credit Risk Analysis in Turkey. 749-759 - Fernando Fernández
, Daniel Borrajo
, Susana Fernández
, David Manzano-Macho:
Assisting Data Mining through Automated Planning. 760-774 - Mikhail Dashevskiy, Zhiyuan Luo:
Predictions with Confidence in Applications. 775-786
Data Mining in Medicine
- Linda C. van der Gaag, Silja Renooij
, Ad Feelders, Arend de Groote, Marinus J. C. Eijkemans, Frank J. Broekmans, Bart C. J. M. Fauser:
Aligning Bayesian Network Classifiers with Medical Contexts. 787-801 - Francisco Reinaldo, Carlos Fernandes, Md. Anishur Rahman, Andreia Malucelli, Rui Camacho
:
Assessing the Eligibility of Kidney Transplant Donors. 802-809 - Cleriston Araujo da Silva, Aristófanes Corrêa Silva
, Stelmo Magalhães Barros Netto, Anselmo Cardoso de Paiva
, Geraldo Braz Junior
, Rodolfo Acatauassu Nunes:
Lung Nodules Classification in CT Images Using Simpson's Index, Geometrical Measures and One-Class SVM. 810-822
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