Proceedings of the Fourth International Conference on Machine Learning and Data Mining in Pattern Recognition
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Petra Perner, Atsushi Imiya
Proceedings of the Fourth International Conference on Machine Learning and Data Mining in Pattern Recognition
MLDM, 2005.

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@proceedings{MLDM-2005,
	address       = "Leipzig, Germany",
	editor        = "Petra Perner and Atsushi Imiya",
	isbn          = "3-540-26923-1",
	publisher     = "{Springer International Publishing}",
	series        = "{Lecture Notes in Computer Science}",
	title         = "{Proceedings of the Fourth International Conference on Machine Learning and Data Mining in Pattern Recognition}",
	volume        = 3587,
	year          = 2005,
}

Contents (68 items)

MLDM-2005-KoK #classification #on the
On ECOC as Binary Ensemble Classifiers (JK, EK), pp. 1–10.
MLDM-2005-GuptaKB #classification #concept analysis #incremental #using
Incremental Classification Rules Based on Association Rules Using Formal Concept Analysis (AG, NK, VB), pp. 11–20.
MLDM-2005-SunWW #algorithm #parametricity
Parameter Inference of Cost-Sensitive Boosting Algorithms (YS, AKCW, YW), pp. 21–30.
MLDM-2005-ZhangZ #component #finite #modelling
Finite Mixture Models with Negative Components (BZ, CZ), pp. 31–41.
MLDM-2005-BouguilaZ #approach #estimation #finite
MML-Based Approach for Finite Dirichlet Mixture Estimation and Selection (NB, DZ), pp. 42–51.
MLDM-2005-MottlKSM #data mining #kernel #mining #multi
Principles of Multi-kernel Data Mining (VM, OK, OS, IBM), pp. 52–61.
MLDM-2005-GhoshGYB #algorithm #analysis #comparative #network #search-based
Comparative Analysis of Genetic Algorithm, Simulated Annealing and Cutting Angle Method for Artificial Neural Networks (RG, MG, JY, AMB), pp. 62–70.
MLDM-2005-GhoshGYB05a #learning #parametricity
Determining Regularization Parameters for Derivative Free Neural Learning (RG, MG, JY, AMB), pp. 71–79.
MLDM-2005-HuysmansBV
A Comprehensible SOM-Based Scoring System (JH, BB, JV), pp. 80–89.
MLDM-2005-TakigawaKN #classification #combinator #product line #set #subclass
The Convex Subclass Method: Combinatorial Classifier Based on a Family of Convex Sets (IT, MK, AN), pp. 90–99.
MLDM-2005-CandillierTTB #clustering #named #statistics
SSC: Statistical Subspace Clustering (LC, IT, FT, OB), pp. 100–109.
MLDM-2005-SzepannekLW #classification #comprehension
Understanding Patterns with Different Subspace Classification (GS, KL, CW), pp. 110–119.
MLDM-2005-EickRBV #assessment #clustering #distance #similarity #using
Using Clustering to Learn Distance Functions for Supervised Similarity Assessment (CFE, AR, AB, RV), pp. 120–131.
MLDM-2005-HaralickH #clustering #linear
Linear Manifold Clustering (RMH, RH), pp. 132–141.
MLDM-2005-NikulinS #clustering #probability
Universal Clustering with Regularization in Probabilistic Space (VN, AJS), pp. 142–152.
MLDM-2005-JanichenP #clustering #concept
Acquisition of Concept Descriptions by Conceptual Clustering (SJ, PP), pp. 153–162.
MLDM-2005-SiaL #clustering #dataset #scalability #using
Clustering Large Dynamic Datasets Using Exemplar Points (WS, MML), pp. 163–173.
MLDM-2005-HalveyKS #clustering #internet #navigation #predict #using
Birds of a Feather Surf Together: Using Clustering Methods to Improve Navigation Prediction from Internet Log Files (MH, MTK, BS), pp. 174–183.
MLDM-2005-GiacintoPR #clustering #detection #network
Alarm Clustering for Intrusion Detection Systems in Computer Networks (GG, RP, FR), pp. 184–193.
MLDM-2005-BarbuHAT #clustering #documentation #graph #image #summary #using
Clustering Document Images Using Graph Summaries (EB, PH, SA, ÉT), pp. 194–202.
MLDM-2005-LegrandN #feature model #using
Feature Selection Method Using Preferences Aggregation (GL, NN), pp. 203–217.
MLDM-2005-Bobrowski #feature model #modelling
Ranked Modelling with Feature Selection Based on the CPL Criterion Functions (LB), pp. 218–227.
MLDM-2005-Boulle #category theory #scalability
A Grouping Method for Categorical Attributes Having Very Large Number of Values (MB), pp. 228–242.
MLDM-2005-ScalzoP #learning #visual notation
Unsupervised Learning of Visual Feature Hierarchies (FS, JHP), pp. 243–252.
MLDM-2005-FerrandizB #graph #multi #recursion
Multivariate Discretization by Recursive Supervised Bipartition of Graph (SF, MB), pp. 253–264.
MLDM-2005-HammoudaMK #clustering #documentation #named
CorePhrase: Keyphrase Extraction for Document Clustering (KMH, DNM, MSK), pp. 265–274.
MLDM-2005-Bak #classification #linear #multi
A New Multidimensional Feature Transformation for Linear Classifiers and Its Applications (EB), pp. 275–284.
MLDM-2005-SilvaPO #comparison
Comparison of FLDA, MLP and SVM in Diagnosis of Lung Nodule (ACS, ACdP, ACMdO), pp. 285–294.
MLDM-2005-SilvaJNP #geometry #learning #metric #using
Diagnosis of Lung Nodule Using Reinforcement Learning and Geometric Measures (ACS, VRdSJ, AdAN, ACdP), pp. 295–304.
MLDM-2005-CaoHXW #algorithm #network #recognition
Iris Recognition Algorithm Based on Point Covering of High-Dimensional Space and Neural Network (WC, JH, GX, SW), pp. 305–313.
MLDM-2005-LiFKL #automation #image #modelling #segmentation #using
Automatic Clinical Image Segmentation Using Pathological Modelling, PCA and SVM (SL, TF, AK, SL), pp. 314–324.
MLDM-2005-KarrasMGO #mining
Improved MRI Mining by Integrating Support Vector Machine Priors in the Bayesian Restoration (DAK, BGM, DGD, DvO), pp. 325–333.
MLDM-2005-KurganH #approach #feature model #predict #sequence
Prediction of Secondary Protein Structure Content from Primary Sequence Alone — A Feature Selection Based Approach (LAK, LH), pp. 334–345.
MLDM-2005-DuKAB #algorithm #clustering #encoding #multi #search-based
Alternative Clustering by Utilizing Multi-objective Genetic Algorithm with Linked-List Based Chromosome Encoding (JD, EEK, RA, KB), pp. 346–355.
MLDM-2005-HayashiMS #classification
Embedding Time Series Data for Classification (AH, YM, NS), pp. 356–365.
MLDM-2005-BunkeDIK #analysis #graph #learning #predict
Analysis of Time Series of Graphs: Prediction of Node Presence by Means of Decision Tree Learning (HB, PJD, CI, MK), pp. 366–375.
MLDM-2005-ShimizuM #anti #sequence
Disjunctive Sequential Patterns on Single Data Sequence and Its Anti-monotonicity (KS, TM), pp. 376–383.
MLDM-2005-PrayR #mining
Mining Expressive Temporal Associations from Complex Data (KAP, CR), pp. 384–394.
MLDM-2005-LaurSNP #data type #statistics
Statistical Supports for Frequent Itemsets on Data Streams (PAL, JES, RN, PP), pp. 395–404.
MLDM-2005-KuhnertK #feedback #learning
Autonomous Vehicle Steering Based on Evaluative Feedback by Reinforcement Learning (KDK, MK), pp. 405–414.
MLDM-2005-DerichsDN #integration #multi #recognition
Cost Integration in Multi-step Viewpoint Selection for Object Recognition (CD, FD, HN), pp. 415–425.
MLDM-2005-LaiST #image #recognition
Support Vector Machine Experiments for Road Recognition in High Resolution Images (JYL, AS, JT), pp. 426–436.
MLDM-2005-ZhaoG #automation #recognition
An Automatic Face Recognition System in the Near Infrared Spectrum (SZ, RRG), pp. 437–444.
MLDM-2005-KinoshenkoMYV #database #image #retrieval #scalability
Hierarchical Partitions for Content Image Retrieval from Large-Scale Database (DK, VM, EY, VV), pp. 445–455.
MLDM-2005-KovalevP #database #image #optimisation
Optimising the Choice of Colours of an Image Database for Dichromats (VAK, MP), pp. 456–465.
MLDM-2005-AdegoriteBKS #approach #mining
An Approach to Mining Picture Objects Based on Textual Cues (AIA, OAB, MSK, KBS), pp. 466–475.
MLDM-2005-LateckiMP #detection #process
Activity and Motion Detection Based on Measuring Texture Change (LJL, RM, DP), pp. 476–486.
MLDM-2005-XiangZCL #approach #recognition #sequence
A New Approach to Human Motion Sequence Recognition with Application to Diving Actions (SX, CZ, XC, NL), pp. 487–496.
MLDM-2005-OhnishiI #analysis #component #detection #independence #using
Dominant Plane Detection Using Optical Flow and Independent Component Analysis (NO, AI), pp. 497–506.
MLDM-2005-BahiS #recognition
Neural Expert Model Applied to Phonemes Recognition (HB, MS), pp. 507–515.
MLDM-2005-LeHS #ambiguity #approach #classification #reasoning #word
An Evidential Reasoning Approach to Weighted Combination of Classifiers for Word Sense Disambiguation (CAL, VNH, AS), pp. 516–525.
MLDM-2005-Sy #approach #detection
Signature-Based Approach for Intrusion Detection (BKS), pp. 526–536.
MLDM-2005-TaniguchiHO #correlation #database #difference #transaction
Discovery of Hidden Correlations in a Local Transaction Database Based on Differences of Correlations (TT, MH, YO), pp. 537–548.
MLDM-2005-YeWWCHT #approach #mining
An Integrated Approach for Mining Meta-rules (FY, JW, SW, HC, TH, LT), pp. 549–557.
MLDM-2005-KuhlmannVLT #data mining #mining #simulation
Data Mining on Crash Simulation Data (AK, RMV, CL, CAT), pp. 558–569.
MLDM-2005-GillamA #mining
Pattern Mining Across Domain-Specific Text Collections (LG, KA), pp. 570–579.
MLDM-2005-MakrehchiK #classification #using
Text Classification Using Small Number of Features (MM, MSK), pp. 580–589.
MLDM-2005-DongKSP #composition #geometry #low level #representation #word
Low-Level Cursive Word Representation Based on Geometric Decomposition (JxD, AK, CYS, DP), pp. 590–599.
MLDM-2005-FerrandizB05a #dataset #evaluation
Supervised Evaluation of Dataset Partitions: Advantages and Practice (SF, MB), pp. 600–609.
MLDM-2005-SilvaK #clustering #distributed
Inference on Distributed Data Clustering (JCdS, MK), pp. 610–619.
MLDM-2005-SharmaVTV #approach #database #mining #multi #novel
A Novel Approach of Multilevel Positive and Negative Association Rule Mining for Spatial Databases (LKS, OPV, UST, RV), pp. 620–629.
MLDM-2005-XiaWZL #data mining #mining #modelling #random
Mixture Random Effect Model Based Meta-analysis for Medical Data Mining (YX, SW, CZ, SL), pp. 630–640.
MLDM-2005-HamanoS #analysis #semantics
Semantic Analysis of Association Rules via Item Response Theory (SH, MS), pp. 641–650.
MLDM-2005-VermaVV #approach #mining #using
Temporal Approach to Association Rule Mining Using T-Tree and P-Tree (KV, OPV, RV), pp. 651–659.
MLDM-2005-HanCY #analysis #component #feature model #image #independence #using
Aquaculture Feature Extraction from Satellite Image Using Independent Component Analysis (JGH, KHC, YKY), pp. 660–666.
MLDM-2005-KotsiantisTRP #modelling
Modeling the Organoleptic Properties of Matured Wine Distillates (SBK, GET, CR, PEP), pp. 667–673.
MLDM-2005-KotsiantisTP #estimation #random
Bagging Random Trees for Estimation of Tissue Softness (SBK, GET, PEP), pp. 674–681.
MLDM-2005-BichindaritzA #concept #mining
Concept Mining for Indexing Medical Literature (IB, SA), pp. 682–691.

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