ICMLA 2007: Cincinnati, Ohio, USA

Invited Speakers

• William W. Cohen:
  Machine Learning for Information Management: Some Promising Directions.
  
• Eric P. Xing:
  Probabilistic Graphical Models-Theory, Algorithm, and Application.
  
• Pierre Baldi:
  Machine Learning Challenges in Chemoinformatics and Drug Screening and Design.
  
• Shamkant B. Navathe:
  Text Mining and Ontology Applications in Bioinformatics and GIS.
  
• Jacek M. Zurada:
  Neural Networks with Complex-Valued Neurons for Recurrent and Feedforward Architectures.
  
• Brendan J. Frey:
  Learning in Biomedicine and Bioinformatics Using Affinity Propagation.
  
• Bernd Wachmann:
  Technologies and Solutions for Trend Detection in Public Literature for Biomarker Discovery.
  

Support Vector Machines

• Yulin Dong, Manghui Tu, Zhonghang Xia, Guangming Xing:
  An optimization method for selecting parameters in support vector machines. 1-6
  
• Thanh-Nghi Do, Jean-Daniel Fekete:
  Large Scale Classification with Support Vector Machine Algorithms. 7-12
  
• Martin Renqiang Min, Anthony J. Bonner, Zhaolei Zhang:
  Modifying kernels using label information improves SVM classification performance. 13-18
  
• Laura Diosan, Alexandrina Rogozan, Jean-Pierre Pécuchet:
  Evolving kernel functions for SVMs by genetic programming. 19-24
  
• Gilles Gasso, Karina Zapien Arreola, Stéphane Canu:
  Sparsity regularization path for semi-supervised SVM. 25-30
  

Evolutionary Methods

• Qiongyun Zhang, Chi Zhou, Weimin Xiao, Peter C. Nelson:
  Improving gene expression programming performance by using differential evolution. 31-37
  
• Amin Shali, Mohammad Reza Kangavari, Bahareh Bina:
  Using genetic programming for the induction of oblique decision trees. 38-43
  
• Michiyuki Hirokane, Shusaku Kouno, Yasutoshi Nomura:
  Extraction of minimum decision algorithm using rough sets and genetic algorithms. 44-49
  
• Guillaume Jouffroy:
  Design of Simple Limit Cycles with Recurrent Neural Networks for Oscillatory Control. 50-55
  
• Ting Qian:
  Multi-Stages Genetic Algorithms: Introducing Temporal Structures to Facilitate Selection of Optimal Evolutionary Paths. 56-61
  
• QingBao Zhu, Shuyan Chen:
  A New Ant Evolution Algorithm to Resolve TSP Problem. 62-66
  

Data Mining

• Haiyun Bian, Raj Bhatnagar, Barrington Young:
  An Efficient Constraint-Based Closed Set Mining Algorithm. 67-72
  
• Mehdi Adda, Lei Wu, Yi Feng:
  Rare Itemset Mining. 73-80
  
• P. Santhi Thilagam, V. S. Ananthanarayana:
  Semantic Partition Based Association Rule Mining across Multiple Databases Using Abstraction. 81-86
  
• Claudio Haruo Yamamoto, Maria Cristina Ferreira de Oliveira, Magaly Lika Fujimoto, Solange Oliveira Rezende:
  An Itemset-Driven Cluster-Oriented Approach to Extract Compact and Meaningful Sets of Association Rules. 87-92
  
• Dadong Yu, Dongbo Liu, Rui Luo, Jianxin Wang:
  Clustering Categorical Data Based on Maximal Frequent Itemsets. 93-97
  

Learning

• Neetha Sebastian, Kamala Krithivasan:
  Learning Algorithms for Grammars of Variable Arity Trees. 98-103
  
• Jason Bobbin:
  An incremental viterbi algorithm. 104-111
  
• Érick Alphonse, Aomar Osmani:
  Phase transition and heuristic search in relational learning. 112-117
  
• Cristina Boicu, Gheorghe Tecuci, Mihai Boicu:
  Learning complex problem solving expertise from failures. 118-123
  
• Guillaume Bouchard:
  Bias-variance tradeoff in hybrid generative-discriminative models. 124-129
  

Text and Multimedia Learning

• Catarina Silva, Bernardete Ribeiro:
  Combining active learning and relevance vector machines for text classification. 130-135
  
• Fei Wang, Xin Wang, Tao Li:
  Efficient label propagation for interactive image segmentation. 136-141
  
• Jia Tse, Christopher Jones, Dean Curtis, Evangelos A. Yfantis:
  An OCR-independent character segmentation using shortest-path in grayscale document images. 142-147
  
• Somnath Banerjee:
  Boosting inductive transfer for text classification using wikipedia. 148-153
  
• Hung-Ching Chen, Mark K. Goldberg, Malik Magdon-Ismail, William A. Wallace:
  Discover the power of social and hidden curriculum to decision making: experiments with enron email and movie newsgroups. 154-159
  

Application I

• Guillaume Bouchard, Jean-Marc Andreoli:
  Soft Failure Detection Using Factorial Hidden Markov Models. 160-165
  
• Andrew Carlson, Ian Fette:
  Memory-based context-sensitive spelling correction at web scale. 166-171
  
• Barrington Young, Raj Bhatnagar, Giridhar Tatavarty, Haiyun Bian:
  Covariance matrix computations with federated databases. 172-177
  
• André Stuhlsatz:
  Recognition of ultrasonic multi-echo sequences for autonomous symbolic indoor tracking. 178-185
  

Learning

• Toshiyuki Sueyoshi, Gopalakrishna Reddy Tadiparthi:
  An agent based system for california electricity market: a perspective of myopic machine learning. 186-191
  
• Shawn Martin, Sean McKenna:
  Predicting building contamination using machine learning. 192-197
  
• Martin Schierle, Sascha Schulz:
  Bootstrapping algorithms for an application in the automotive domain. 198-203
  

Classification

• Lofton A. Bullard, Taghi M. Khoshgoftaar, Kehan Gao:
  An application of a rule-based model in software quality classification. 204-210
  
• Henrik Boström:
  Estimating class probabilities in random forests. 211-216
  
• Robert H. Fujii, Taiki Ichishita:
  Effect of synaptic weight assignment on spiking neuron response. 217-222
  

Application II

• Dianwei Han, Jun Zhang:
  A comparison of two algorithms for predicting the condition number. 223-228
  
• Thomas J. Mitchell, David P. Creasey:
  Evolutionary Sound Matching: A Test Methodology and Comparative Study. 229-234
  
• Slawomir Nowaczyk, Jacek Malec:
  Learning to evaluate conditional partial plans. 235-240
  

Regression/Prediction

• Balaji Kommineni, Shubhankar Basu, Ranga Vemuri:
  A spline based regression technique on interval valued noisy data. 241-247
  
• Yang Lan, Daniel Neagu:
  A new time series prediction algorithm based on moving average of nth-order difference. 248-253
  
• David Thornley, Maxim Zverev, Stavros Petridis:
  Machine learned regression for abductive DNA sequencing. 254-259
  
• Jian-Wu Xu, Shipeng Yu, Jinbo Bi, Lucian Vlad Lita, Radu Stefan Niculescu, R. Bharat Rao:
  Automatic medical coding of patient records via weighted ridge regression. 260-265
  
• Adeline Schmitz, Hamid Hefazi:
  Constructive neural network ensemble for regression tasks in high dimensional spaces. 266-273
  

Machine Learning in Web Based Real-Time Applications

• Nihat Yilmaz, Seref Sagiroglu:
  Web-based maze robot learning using fuzzy motion control system. 274-279
  
• Seref Sagiroglu, Uraz Yavanoglu, Esra Nergis Guven:
  Web based machine learning for language identification and translation. 280-285
  
• Hamdi Tolga Kahraman, Ilhami Colak, Seref Sagiroglu:
  A web based adaptive educational system. 286-291
  

Dimensionality Reduction

• Michael Davy, Saturnino Luz:
  Dimensionality reduction for active learning with nearest neighbour classifier in text categorisation problems. 292-297
  
• Yiming Wu, Xiuwen Liu, Washington Mio:
  Scalable optimal linear representation for face and object recognition. 298-303
  

Induction, Model Selection and Evaluation

• Chunsheng Yang, Sylvain Létourneau:
  Model evaluation for prognostics: estimating cost saving for the end users. 304-309
  
• Jung Min Kong, Dong-Hun Seo, Won Don Lee:
  Rule refinement with extended data expression. 310-315
  
• Guénaël Cabanes, Younès Bennani:
  A simultaneous two-level clustering algorithm for automatic model selection. 316-321
  
• Peter Owotoki, Friedrich Mayer-Lindenberg:
  LEONARDO - The computational intelligence (CI) model selection wizard. 322-329
  
• José A. Ramírez-Hernández, Emmanuel Fernandez:
  Control of a re-entrant line manufacturing model with a reinforcement learning approach. 330-335
  

Reinforcement Learning/ Learning from Imbalanced Data/Bayesian Networks

• Victor Uc Cetina:
  Supervised reinforcement learning using behavior models. 336-341
  
• Jan Hendrik Metzen, Mark Edgington, Yohannes Kassahun, Frank Kirchner:
  Performance evaluation of EANT in the robocup keepaway benchmark. 342-347
  
• Taghi M. Khoshgoftaar, Chris Seiffert, Jason Van Hulse, Amri Napolitano, Andres Folleco:
  Learning with limited minority class data. 348-353
  
• Manuel Stritt, Lars Schmidt-Thieme, Gerhard Poeppel:
  Combining multi-distributed mixture models and bayesian networks for semi-supervised learning. 354-362
  
• Dennis J. Drown, Taghi M. Khoshgoftaar, Ramaswamy Narayanan:
  Using evolutionary sampling to mine imbalanced data. 363-368
  
• Alexandra M. Carvalho, Arlindo L. Oliveira:
  Learning bayesian networks consistent with the optimal branching. 369-374
  

Unsupervised Learning

• Qiyong Guo, Hongyu Li, Wenbin Chen, I-Fan Shen, Jussi Parkkinen:
  Manifold clustering via energy minimization. 375-380
  
• Jian Zhang:
  Co-clustering by similarity refinement. 381-386
  
• Thouraya Ayadi, Tarek M. Hamdani, Adel M. Alimi, Mohamed A. Khabou:
  2IBGSOM: interior and irregular boundaries growing self-organizing maps. 387-392
  
• Bülent Yücesoy, Sule Gündüz Ögüdücü:
  Comparison of semantic and single term similarity measures for clustering turkish documents. 393-398
  
• Dong Moon Kim, Kunsu Kim, Kyo-Hyun Park, Jee-Hyong Lee, Keon-Myung Lee:
  A music recommendation system with a dynamic k-means clustering algorithm. 399-403
  

Feature Extraction and Selection / Ensemble Learning

• Sasthakumar Ramamurthy, Raj Bhatnagar:
  Tracking recurrent concept drift in streaming data using ensemble classifiers. 404-409
  
• Viktoria Rojkova, Mehmed M. Kantardzic:
  Feature extraction using random matrix theory approach. 410-416
  
• Lei Yu, Hao Li:
  Toward optimal selection of feature clusters. 417-422
  
• Dean Curtis, Vitaliy Kubushyn, Evangelos A. Yfantis, Michael Rogers:
  A hierarchical feature decomposition clustering algorithm for unsupervised classification of document image types. 423-428
  
• Xue-wen Chen, Jong Cheol Jeong:
  Enhanced recursive feature elimination. 429-435
  

Application III

• Johannes Hartz, Bernd Neumann:
  Learning a knowledge base of ontological concepts for high-level scene interpretation. 436-443
  
• Bo Li, Juan Liu, Wenjuan Shi:
  Web-based parallel corpora for statistical machine translation. 444-449
  
• Volker Willert, Marc Toussaint, Julian Eggert, Edgar Körner:
  Uncertainty optimization for robust dynamic optical flow estimation. 450-457
  
• Brian Burford:
  Direct calculation of predictions for K29/K29. 458-463
  
• Mehdi Samadi, Zohreh Azimifar, Mansour Zolghadri Jahromi:
  Learning: An Effective Approach in Endgame Chess Board Evaluation. 464-469
  
• Mohmad Marouf Wani, M. Arif Wani:
  Hybrid Neural Network Based Model for Predicting the Performance of a Two Stroke Spark Ignition Engine. 470-475
  

Machine Learning Applications in Bioinformatics

• Yi Sun, Mark Robinson, Rod Adams, Neil Davey, Alistair G. Rust:
  Predicting Binding Sites in the Mouse Genome. 476-481
  
• Glenn Fung, Renaud. Seigneuric, Sriram Krishnan, R. Bharat Rao, Brad G. Wouters, Philippe Lambin:
  Reducing a Biomarkers List via Mathematical Programming: Application to Gene Signatures to Detect Time-Dependent Hypoxia in Cancer. 482-487
  
• Michalis E. Blazadonakis, Michalis E. Zervakis:
  Polynomial and RBF Kernels as Marker Selection Tools-A Breast Cancer Case Study. 488-493
  
• Daniele Yumi Sunaga, Júlio C. Nievola, Milton Pires Ramos:
  Statistical and Biological Validation Methods in Cluster Analysis of Gene Expression. 494-499
  
• Bobbie-Jo M. Webb-Robertson, Christopher S. Oehmen, William R. Cannon:
  Support Vector Machine Classification of Probability Models and Peptide Features for Improved Peptide Identification from Shotgun Proteomics. 500-505
  
• Amit U. Sinha, Mukta Phatak, Raj Bhatnagar, Anil G. Jegga:
  Identifying Functional Binding Motifs of Tumor Protein p53 Using Support Vector Machines. 506-511
  

Workshop Session: Machine Learning Applications in Biomedicine

• Claire L. McCullough, Andrew J. Novobilski, Francis M. Fesmire:
  Use of Neural Networks to Predict Adverse Outcomes from Acute Coronary Syndrome for Male and Female Patients. 512-517
  
• Janusz Wojtusiak, Ryszard S. Michalski, Thipkesone Simanivanh, Anna V. Baranova:
  The Natural Induction System AQ21 and its Application to Data Describing Patients with Metabolic Syndrome: Initial Results. 518-523
  
• Sang-Chul Lee, Peter Bajcsy:
  Understanding Challenges in Preserving and Reconstructing Computer-Assisted Medical Decision Processes. 524-529
  
• Rómer Rosales, Praveen Krishnamurthy, R. Bharat Rao:
  Semi-Supervised Active Learning for Modeling Medical Concepts from Free Text. 530-536
  
• Alexander J. Senf, Carl Leonard, James M. DeLeo:
  A Statistical Algorithm to Discover Knowledge in Medical Data Sources. 537-540
  
• Kai Xing, Donald Henson, Dechang Chen, Li Sheng:
  A Clustering-Based Approach to Predict Outcome in Cancer Patients. 541-546
  

Workshop Session: Machine Learning Applications in Genomics

• Mingzhou (Joe) Song, Robert M. Haralick, Stéphane Boissinot:
  Maximum Likelihood Quantization of Genomic Features Using Dynamic Programming. 547-553
  
• Kwangmin Choi, Youngik Yang, Sun Kim:
  CLASSEQ: Classification of Sequences via Comparative Analysis of Multiple Genomes. 554-559
  
• Li Chen, Chen Wang, Le-Ming Shih, Tian-Li Wang, Zhen Zhang, Yue Wang, Robert Clarke, Eric Hoffman, Jianhua Xuan:
  Biomarker Identification by Knowledge-Driven Multi-Level ICA and Motif Analysis. 560-566
  
• Dietmar H. Dorr, Anne Denton:
  Generalized Sequence Signatures through Symbolic Clustering. 567-572
  
• Mark A. Kon, Yue Fan, Dustin T. Holloway, Charles DeLisi:
  SVMotif: A Machine Learning Motif Algorithm. 573-580
  

Workshop Session: Machine Learning Applications in Proteomics

• Thomas Villmann, Frank-Michael Schleif, Martijn van der Werff, André M. Deelder, Rob A. E. M. Tollenaar:
  Association Learning in SOMs for Fuzzy-Classification. 581-586
  
• Ronald C. Taylor, Mudita Singhal, Don Simone Daly, Kelly Domico, Amanda M. White, Deanna L. Auberry, Kenneth J. Auberry, Brian Hooker, Gregory B. Hurst, Jason McDermott, W. Hayes McDonald, Dale Pelletier, Denise Schmoyer, William R. Cannon:
  SEBINI-CABIN: An Analysis Pipeline for Biological Network Inference, with a Case Study in Protein-Protein Interaction Network Reconstruction. 587-593
  
• Jia Song, Chunmei Liu, Yinglei Song, Junfeng Qu:
  Alignment of Multiple Proteins with an Ensemble of Hidden Markov Models. 594-599
  
• Dongxiao Zhu, Hua Li:
  Improvement of Bayesian Network Inference Using a Relaxed Gene Ordering. 600-605
  

Workshop Session: Machine Learning Applications in Transcriptomics

• Young-Rae Cho, Xian Xu, Woochang Hwang, Aidong Zhang:
  Feature Extraction from Microarray Expression Data by Integration of Semantic Knowledge. 606-611
  
• Huilin Xiong, Ya Zhang, Xue-wen Chen:
  Normalized Linear Transform for Cross-Platform Microarray Data Integration. 612-617
  
• Leif E. Peterson, Matthew Coleman:
  Logistic Ensembles for Random Spherical Linear Oracles. 618-623
  
• Zhenqiu Liu:
  Cox's Proportional Hazards Model with Lp Penalty for Biomarker Identification and Survival Prediction. 624-628
  
• Lynne Billard, Duck-Ki Kim, Chan-Hee Lee, Sung Duck Lee, Keon-Myung Lee, Sung-Soo Kim:
  Modeling Spatial-Temporal Epidemics Using STBL Model. 629-633