Proceedings of the Sixth International Conference on Machine Learning and Data Mining in Pattern Recognition
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Petra Perner
Proceedings of the Sixth International Conference on Machine Learning and Data Mining in Pattern Recognition
MLDM, 2009.

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@proceedings{MLDM-2009,
	address       = "Leipzig, Germany",
	doi           = "10.1007/978-3-642-03070-3",
	editor        = "Petra Perner",
	isbn          = "978-3-642-03069-7",
	publisher     = "{Springer International Publishing}",
	series        = "{Lecture Notes in Computer Science}",
	title         = "{Proceedings of the Sixth International Conference on Machine Learning and Data Mining in Pattern Recognition}",
	volume        = 5632,
	year          = 2009,
}

Contents (61 items)

MLDM-2009-Truemper #reliability #strict
Improved Comprehensibility and Reliability of Explanations via Restricted Halfspace Discretization (KT), pp. 1–15.
MLDM-2009-SeredinKM #machine learning #order #set
Selection of Subsets of Ordered Features in Machine Learning (OS, AK, VM), pp. 16–28.
MLDM-2009-KurasovaM #visualisation
Combination of Vector Quantization and Visualization (OK, AM), pp. 29–43.
MLDM-2009-MorelandT
Discretization of Target Attributes for Subgroup Discovery (KM, KT), pp. 44–52.
MLDM-2009-ZhuFF #classification #privacy
Preserving Privacy in Time Series Data Classification by Discretization (YZ, YF, HF), pp. 53–67.
MLDM-2009-QureshiZ #quality #using
Using Resampling Techniques for Better Quality Discretization (TQ, DAZ), pp. 68–81.
MLDM-2009-LiuYZZZL #classification #scalability
A Large Margin Classifier with Additional Features (XL, JY, EZ, GZ, YZ, ML), pp. 82–95.
MLDM-2009-HasanG #adaptation #classification #modelling
Sequential EM for Unsupervised Adaptive Gaussian Mixture Model Based Classifier (BASH, JQG), pp. 96–106.
MLDM-2009-WangZ #classification #kernel
Optimal Double-Kernel Combination for Classification (FW, HZ), pp. 107–122.
MLDM-2009-MoedS #classification #performance
Efficient AdaBoost Region Classification (MM, ENS), pp. 123–136.
MLDM-2009-KobayashiS #classification #distributed #linear #representation #using
A Linear Classification Method in a Very High Dimensional Space Using Distributed Representation (TK, IS), pp. 137–147.
MLDM-2009-StahlBA #classification #composition #framework #induction #named #parallel
PMCRI: A Parallel Modular Classification Rule Induction Framework (FTS, MAB, MA), pp. 148–162.
MLDM-2009-TronciGR
Dynamic Score Combination: A Supervised and Unsupervised Score Combination Method (RT, GG, FR), pp. 163–177.
MLDM-2009-BarinovaV #named
ODDboost: Incorporating Posterior Estimates into AdaBoost (OB, DV), pp. 178–190.
MLDM-2009-Mendes-MoreiraJSS #approach #case study #learning
Ensemble Learning: A Study on Different Variants of the Dynamic Selection Approach (JMM, AMJ, CS, JFdS), pp. 191–205.
MLDM-2009-DuangsoithongW #analysis #classification
Relevance and Redundancy Analysis for Ensemble Classifiers (RD, TW), pp. 206–220.
MLDM-2009-RosenthalVHHL
Drift-Aware Ensemble Regression (FR, PBV, MH, DH, WL), pp. 221–235.
MLDM-2009-LiHLG #concept #detection #random #streaming
Concept Drifting Detection on Noisy Streaming Data in Random Ensemble Decision Trees (PPL, XH, QL, YG), pp. 236–250.
MLDM-2009-LoglisciM #mining #multi
Mining Multiple Level Non-redundant Association Rules through Two-Fold Pruning of Redundancies (CL, DM), pp. 251–265.
MLDM-2009-MendesA #approach #mining #natural language
Pattern Mining with Natural Language Processing: An Exploratory Approach (ACM, CA), pp. 266–279.
MLDM-2009-FranceC #distance
Is the Distance Compression Effect Overstated? Some Theory and Experimentation (SLF, JDC), pp. 280–294.
MLDM-2009-SegataB #dataset #performance #scalability
Fast Local Support Vector Machines for Large Datasets (NS, EB), pp. 295–310.
MLDM-2009-BarakatB
The Effect of Domain Knowledge on Rule Extraction from Support Vector Machines (NHB, APB), pp. 311–321.
MLDM-2009-ElghazelB #towards
Towards B-Coloring of SOM (HE, KB), pp. 322–336.
MLDM-2009-SmaouiMM #algorithm #assessment #clustering #named #quality
CSBIterKmeans: A New Clustering Algorithm Based on Quantitative Assessment of the Clustering Quality (TS, SM, CMS), pp. 337–346.
MLDM-2009-CzarnowskiJ #clustering #distributed
Agent-Based Non-distributed and Distributed Clustering (IC, PJ), pp. 347–360.
MLDM-2009-AbdalaJ #approach #clustering
An Evidence Accumulation Approach to Constrained Clustering Combination (DDA, XJ), pp. 361–371.
MLDM-2009-SakaiI #clustering #performance #random
Fast Spectral Clustering with Random Projection and Sampling (TS, AI), pp. 372–384.
MLDM-2009-Hoppner #how #question
How Much True Structure Has Been Discovered? (FH), pp. 385–397.
MLDM-2009-SarmentoKOU #clustering #performance #set
Efficient Clustering of Web-Derived Data Sets (LS, AK, ECO, LHU), pp. 398–412.
MLDM-2009-BenabdeslemS #approach #clustering #probability
A Probabilistic Approach for Constrained Clustering with Topological Map (KB, JS), pp. 413–426.
MLDM-2009-CeciALCFVM #data type #detection #mining #relational
Relational Frequent Patterns Mining for Novelty Detection from Data Streams (MC, AA, CL, CC, FF, CV, DM), pp. 427–439.
MLDM-2009-IsakssonD #algorithm #case study #comparative #detection
A Comparative Study of Outlier Detection Algorithms (CI, MHD), pp. 440–453.
MLDM-2009-Mejia-LavalleV #detection
Outlier Detection with Explanation Facility (MML, ASV), pp. 454–464.
MLDM-2009-BouthinonSV #ambiguity #concept #learning
Concept Learning from (Very) Ambiguous Examples (DB, HS, VV), pp. 465–478.
MLDM-2009-OkuboH #concept #pseudo
Finding Top-N Pseudo Formal Concepts with Core Intents (YO, MH), pp. 479–493.
MLDM-2009-Calliess #on the
On Fixed Convex Combinations of No-Regret Learners (JPC), pp. 494–504.
MLDM-2009-YuksekC #algorithm
An Improved Tabu Search (ITS) Algorithm Based on Open Cover Theory for Global Extremums (KY, SC), pp. 505–515.
MLDM-2009-MorelandT09a #problem
The Needles-in-Haystack Problem (KM, KT), pp. 516–524.
MLDM-2009-NikolopoulosPKP #comprehension #framework #image #probability #semantics
An Evidence-Driven Probabilistic Inference Framework for Semantic Image Understanding (SN, GTP, IK, IP), pp. 525–539.
MLDM-2009-NunesSP #detection #image #using
Detection of Masses in Mammographic Images Using Simpson’s Diversity Index in Circular Regions and SVM (APN, ACS, ACdP), pp. 540–553.
MLDM-2009-KovalevPV #image #mining
Mining Lung Shape from X-Ray Images (VK, AP, PV), pp. 554–568.
MLDM-2009-XiongBS #detection
A Wavelet-Based Method for Detecting Seismic Anomalies in Remote Sensing Satellite Data (PX, YB, XS), pp. 569–581.
MLDM-2009-LiuSQ
Spectrum Steganalysis of WAV Audio Streams (QL, AHS, MQ), pp. 582–593.
MLDM-2009-FersiniMAA #approach #multi #recognition
Audio-Based Emotion Recognition in Judicial Domain: A Multilayer Support Vector Machines Approach (EF, EM, GA, FA), pp. 594–602.
MLDM-2009-LeeCWL #learning
Learning with a Quadruped Chopstick Robot (WCL, JCC, SzW, KML), pp. 603–616.
MLDM-2009-RiesenB #difference #graph #prototype #reduction #using
Dissimilarity Based Vector Space Embedding of Graphs Using Prototype Reduction Schemes (KR, HB), pp. 617–631.
MLDM-2009-GoncalvesQ #classification #kernel #semantics #using
Using Graph-Kernels to Represent Semantic Information in Text Classification (TG, PQ), pp. 632–646.
MLDM-2009-JingWYX #categorisation #feature model #framework
A General Framework of Feature Selection for Text Categorization (HJ, BW, YY, YX), pp. 647–662.
MLDM-2009-GadK #clustering #semantics #similarity #using
New Semantic Similarity Based Model for Text Clustering Using Extended Gloss Overlaps (WKG, MSK), pp. 663–677.
MLDM-2009-StrumbeljRK #learning
Learning Betting Tips from Users’ Bet Selections (ES, MRS, IK), pp. 678–688.
MLDM-2009-SarmentoKOU09a #ambiguity #approach
An Approach to Web-Scale Named-Entity Disambiguation (LS, AK, ECO, LHU), pp. 689–703.
MLDM-2009-ChanguelLB #automation #html #learning
A General Learning Method for Automatic Title Extraction from HTML Pages (SC, NL, BBM), pp. 704–718.
MLDM-2009-CelepcikayEO #dataset #using
Regional Pattern Discovery in Geo-referenced Datasets Using PCA (OUC, CFE, CO), pp. 719–733.
MLDM-2009-NikovskiR #modelling #predict
Memory-Based Modeling of Seasonality for Prediction of Climatic Time Series (DN, GR), pp. 734–748.
MLDM-2009-DereliogluGO #analysis #approach
A Neural Approach for SME’s Credit Risk Analysis in Turkey (GD, FSG, NO), pp. 749–759.
MLDM-2009-FernandezBFM #automation #data mining #mining
Assisting Data Mining through Automated Planning (FF, DB, SF, DMM), pp. 760–774.
MLDM-2009-DashevskiyL #predict
Predictions with Confidence in Applications (MD, ZL), pp. 775–786.
MLDM-2009-GaagRFGEBF #classification #network
Aligning Bayesian Network Classifiers with Medical Contexts (LCvdG, SR, AF, AdG, MJCE, FJB, BCJMF), pp. 787–801.
MLDM-2009-ReinaldoFRMC
Assessing the Eligibility of Kidney Transplant Donors (FR, CF, MAR, AM, RC), pp. 802–809.
MLDM-2009-SilvaSNPJN #classification #geometry #image #metric #using
Lung Nodules Classification in CT Images Using Simpson’s Index, Geometrical Measures and One-Class SVM (CAdS, ACS, SMBN, ACdP, GBJ, RAN), pp. 810–822.

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