Petra Perner
Proceedings of the 11th International Conference on Machine Learning and Data Mining in Pattern Recognition
MLDM, 2015.

@proceedings{MLDM-2015,
	address       = "Hamburg, Germany",
	doi           = "10.1007/978-3-319-21024-7",
	editor        = "Petra Perner",
	isbn          = "978-3-319-21023-0",
	publisher     = "{Springer International Publishing}",
	series        = "{Lecture Notes in Computer Science}",
	title         = "{Proceedings of the 11th International Conference on Machine Learning and Data Mining in Pattern Recognition}",
	volume        = 9166,
	year          = 2015,
}

Contents (31 items)

7 ×#using
6 ×#mining
5 ×#analysis
5 ×#clustering
5 ×#learning
4 ×#approach
4 ×#graph
4 ×#named
4 ×#performance
3 ×#algorithm

MLDM-2015-RiesenFDB #distance #edit distance #graph

Greedy Graph Edit Distance (KR, MF, RD, HB), pp. 3–16.

MLDM-2015-FerrerSR #approximate #distance #edit distance #graph #heuristic #learning

Learning Heuristics to Reduce the Overestimation of Bipartite Graph Edit Distance Approximation (MF, FS, KR), pp. 17–31.

MLDM-2015-DhulekarNOY #graph #learning #mining #predict

Seizure Prediction by Graph Mining, Transfer Learning, and Transformation Learning (ND, SN, BO, BY), pp. 32–52.

MLDM-2015-DavidsenSP #classification #fuzzy #search-based

Local and Global Genetic Fuzzy Pattern Classifiers (SAD, ES, MP), pp. 55–69.

MLDM-2015-TanGZ #incremental #kernel #named

IKLTSA: An Incremental Kernel LTSA Method (CT, JG, SZ), pp. 70–83.

MLDM-2015-ShaluntsB #analysis #named #sentiment

SentiSAIL: Sentiment Analysis in English, German and Russian (GS, GB), pp. 87–97.

MLDM-2015-CoralloFMACCGS #analysis #approach #sentiment

Sentiment Analysis for Government: An Optimized Approach (AC, LF, MM, MA, AC, VC, EG, DS), pp. 98–112.

MLDM-2015-IshayH #algorithm #clustering #integration #novel

A Novel Algorithm for the Integration of the Imputation of Missing Values and Clustering (RBI, MH), pp. 115–129.

MLDM-2015-TaT #algorithm #database #owl #relational

Improving the Algorithm for Mapping of OWL to Relational Database Schema (CDCT, TPT), pp. 130–139.

MLDM-2015-KarkkainenS #analysis #component #robust

Robust Principal Component Analysis of Data with Missing Values (TK, MS), pp. 140–154.

MLDM-2015-ZidaFWLT #mining #performance

Efficient Mining of High-Utility Sequential Rules (SZ, PFV, CWW, JCWL, VST), pp. 157–171.

MLDM-2015-Prado #classification #named

MOGACAR: A Method for Filtering Interesting Classification Association Rules (DBP), pp. 172–183.

MLDM-2015-CostaFK #multi #using

Classifying Grasslands and Cultivated Pastures in the Brazilian Cerrado Using Support Vector Machines, Multilayer Perceptrons and Autoencoders (WC, LMGF, TSK), pp. 187–198.

MLDM-2015-GovadaJMS #approach #hybrid #induction #learning #using

Hybrid Approach for Inductive Semi Supervised Learning Using Label Propagation and Support Vector Machine (AG, PJ, SM, SKS), pp. 199–213.

MLDM-2015-SalahAM #mining #optimisation #performance #pipes and filters

Optimizing the Data-Process Relationship for Fast Mining of Frequent Itemsets in MapReduce (SS, RA, FM), pp. 217–231.

MLDM-2015-AkbariniaM #probability #streaming

Aggregation-Aware Compression of Probabilistic Streaming Time Series (RA, FM), pp. 232–247.

MLDM-2015-MojahedBWI #analysis #clustering #matrix #semistructured data #similarity #using

Applying Clustering Analysis to Heterogeneous Data Using Similarity Matrix Fusion (SMF) (AM, JHBS, WW, BdlI), pp. 251–265.

MLDM-2015-OliveiraVZ #clustering #on the

On Bicluster Aggregation and its Benefits for Enumerative Solutions (SHGdO, RV, FJVZ), pp. 266–280.

MLDM-2015-TreechalongRW #clustering #using

Semi-Supervised Stream Clustering Using Labeled Data Points (KT, TR, KW), pp. 281–295.

MLDM-2015-AmalamanE #algorithm #clustering #named

Avalanche: A Hierarchical, Divisive Clustering Algorithm (PKA, CFE), pp. 296–310.

MLDM-2015-PatchalaBG #email #using

Author Attribution of Email Messages Using Parse-Tree Features (JP, RB, SG), pp. 313–327.

MLDM-2015-SejalSTAVIP #graph #query #similarity

Query Click and Text Similarity Graph for Query Suggestions (DS, KGS, VT, DA, KRV, SSI, LMP), pp. 328–341.

MLDM-2015-Babu #classification #identification #using

Offline Writer Identification in Tamil Using Bagged Classification Trees (SB), pp. 342–354.

MLDM-2015-AlzahraniAAB #data analysis

Data Analysis for Courses Registration (NA, RA, NA, GB), pp. 357–367.

MLDM-2015-MoldovanM #data mining #learning #mining #performance #using

Learning the Relationship Between Corporate Governance and Company Performance Using Data Mining (DM, SM), pp. 368–381.

MLDM-2015-KrasotkinaM #approach #optimisation #ranking

A Bayesian Approach to Sparse Learning-to-Rank for Search Engine Optimization (OK, VM), pp. 382–394.

MLDM-2015-Chou #data-driven #geometry #learning

Data Driven Geometry for Learning (EPC), pp. 395–402.

MLDM-2015-Al-SaleemAAB #education #mining #performance #predict #student

Mining Educational Data to Predict Students’ Academic Performance (MAS, NAK, SAO, GB), pp. 403–414.

MLDM-2015-RibeiroOFVC #modelling

Patient-Specific Modeling of Medical Data (GASR, ACMdO, ALSF, SV, GFC), pp. 415–424.

MLDM-2015-KrasotkinaM15a #analysis #approach

A Bayesian Approach to Sparse Cox Regression in High-Dimentional Survival Analysis (OK, VM), pp. 425–437.

MLDM-2015-Perner #automation #feature model #image #mining

Automatic Cell Tracking and Kinetic Feature Description of Cell Paths for Image Mining (PP), pp. 441–451.