• 
  Sorin Draghici, Taghi M. Khoshgoftaar, Vasile Palade, Witold Pedrycz, M. Arif Wani, Xingquan Zhu (Eds.): The Ninth International Conference on Machine Learning and Applications, ICMLA 2010, Washington, DC, USA, 12-14 December 2010. IEEE Computer Society 2010 ISBN 978-0-7695-4300-0

Data Acquisition, Cleansing and Categorization

• 
  David J. Miller, Chu-Fang Lin, George Kesidis, Christopher M. Collins: Improved Fine-Grained Component-Conditional Class Labeling with Active Learning. 3-8
• 
  Jason Van Hulse, Taghi M. Khoshgoftaar, Amri Napolitano: A Novel Noise Filtering Algorithm for Imbalanced Data. 9-14
• 
  Shayok Chakraborty, Vineeth Nallure Balasubramanian, Sethuraman Panchanathan: Dynamic Batch Size Selection for Batch Mode Active Learning in Biometrics. 15-22
• 
  Dan Shen, Jean-David Ruvini, Rajyashree Mukherjee, Neel Sundaresan: A Study of Smoothing Algorithms for Item Categorization on e-Commerce Sites. 23-28

Planning and Reinforcement Learning

• 
  Yong Lin, Fillia Makedon: From Serve-on-Demand to Serve-on-Need: A Game Theoretic Approach. 31-36
• 
  Elizabeth Duane S. Costa, Maury M. Gouvea Jr.: Autonomous Navigation in Dynamic Environments with Reinforcement Learning and Heuristic. 37-42
• 
  Man Chon U, Zhen Li: Public Goods Game Simulator with Reinforcement Learning Agents. 43-49
• 
  Philip R. Cook, Michael A. Goodrich: MMM-PHC: A Particle-Based Multi-Agent Learning Algorithm. 50-55

Supervised Learning

• 
  Joaquim F. Pinto da Costa, Ricardo Sousa, Jaime S. Cardoso: An All-at-once Unimodal SVM Approach for Ordinal Classification. 59-64
• 
  Abdullah Bawakid, Mourad Oussalah: Centroid-based Classification Enhanced with Wikipedia. 65-70
• 
  Jaime S. Cardoso, Ricardo Sousa: Classification Models with Global Constraints for Ordinal Data. 71-77
• 
  Qi Li, Chang-Tien Lu: Appearance Based Recognition Using Spatial and Discriminant Influence. 78-83
• 
  Madhavi Vedula Ratnagiri, Lawrence R. Rabiner, Biing-Hwang Juang: Multi-Class Classification Using a New Sigmoid Loss Function for Minimum Classification Error (MCE). 84-89

Multi-party, Multi-modal, Multi-objective Learning

• 
  Nesrine Elfelly, Jean-Yves Dieulot, Pierre Borne, Mohamed Benrejeb: A Multimodel Approach of Complex Systems Identification and Control Using Neural and Fuzzy Clustering Algorithms. 93-98
• 
  Cynthia L. Johnson, Avelino J. Gonzalez: Learning Collaborative Behavior by Observation. 99-104
• 
  Jeff Allen, John Anderson: Heterogeneous Imitation Learning from Demonstrators of Varying Physiology and Skill. 105-112
• 
  Frank Sehnke, Alex Graves, Christian Osendorfer, Jürgen Schmidhuber: Multimodal Parameter-exploring Policy Gradients. 113-118
• 
  Rafael Giusti, Gustavo E. A. P. A. Batista: Discovering Knowledge Rules with Multi-Objective Evolutionary Computing. 119-124

Feature Selection

• 
  Qin Yang, Robin Gras: How Dependencies Affect the Capability of Several Feature Selection Approaches to Extract the Key Features. 127-134
• 
  Huanjing Wang, Taghi M. Khoshgoftaar, Amri Napolitano: A Comparative Study of Ensemble Feature Selection Techniques for Software Defect Prediction. 135-140
• 
  Sascha Klement, Thomas Martinetz: A New Approach to Classification with the Least Number of Features. 141-146
• 
  David J. Dittman, Taghi M. Khoshgoftaar, Randall Wald, Jason Van Hulse: Comparative Analysis of DNA Microarray Data through the Use of Feature Selection Techniques. 147-152

Probabilistic and Model Based Learning

• 
  Chen-Hsiang Yeang: A Probabilistic Graphical Model of Quantum Systems. 155-162
• 
  Quang-Thang Dinh, Matthieu Exbrayat, Christel Vrain: Heuristic Method for Discriminative Structure Learning of Markov Logic Networks. 163-168
• 
  Joseph Defretin, Julien Marzatz, Helene Piet-Lahanierz: Learning Viewpoint Planning in Active Recognition on a Small Sampling Budget: A Kriging Approach. 169-174
• 
  Mohamed Nadif, Gérard Govaert: Model-Based Co-clustering for Continuous Data. 175-180

Similarity Learning for Pattern Recognition

• 
  Ali Mustafa Qamar, Éric Gaussier: Similarity Learning in Nearest Neighbor and Relief Algorithm. 183-189
• 
  Syed Fawad Hussain, Gilles Bisson, Clement Grimal: An Improved Co-Similarity Measure for Document Clustering. 190-197
• 
  Ce Gao, Yixu Song, Peifa Jia: Multilayer Ferns: A Learning-based Approach of Patch Recognition and Homography Extraction. 198-203
• 
  Qi Li, Zhonghang Xia, Guangming Xing: A Binocular Framework for Face Liveness Verification under Unconstrained Localization. 204-207
• 
  Qi Li, Vojislav Kecman, Raied Salman: A Chunking Method for Euclidean Distance Matrix Calculation on Large Dataset Using Multi-GPU. 208-213

Kernel Learning Methods

• 
  Richard E. Hudson, Jeremy A. Marvel, Wyatt S. Newman: Modeling and Training Radial Basis Functions with Integrate-and-Fire Neurons. 217-222
• 
  Hideitsu Hino, Nima Reyhani, Noboru Murata: Multiple Kernel Learning by Conditional Entropy Minimization. 223-228
• 
  Zhonghang Xia, Wenke Zhang, Manghui Tu, I-Ling Yen: Kernel-based Approaches for Collaborative Filtering. 229-234
• 
  Vineeth Nallure Balasubramanian, Shayok Chakraborty, Sethuraman Panchanathan, Jieping Ye: Kernel Learning for Efficiency Maximization in the Conformal Predictions Framework. 235-242
• 
  Tim vor der Brück, Hermann Helbig: Validating Meronymy Hypotheses with Support Vector Machines and Graph Kernels. 243-250

Unsupervised Learning

• 
  Sai Venu Gopal Lolla, Lawrence L. Hoberock: Improved Unsupervised Clustering over Watershed-Based Clustering. 253-259
• 
  Tianming Hu, Chuanren Liu, Jing Sun, Sam Yuan Sung, Peter A. Ng: Pairwise Constrained Clustering with Group Similarity-Based Patterns. 260-265
• 
  Harry Strange, Reyer Zwiggelaar: Parallel Projections for Manifold Learning. 266-271
• 
  Zhi Qiao, Peng Zhang, Jing He, Jinghua Yan, Li Guo: Learning from Multiple Related Data Streams with Asynchronous Flowing Speeds. 272-277

Machine Learning in Bioinformatics and Computational Biology

• 
  Jinghua Gu, Jianhua Xuan, Yue Joseph Wang, Rebecca B. Riggins, Robert Clarke: Identification of Transcriptional Regulatory Networks by Learning the Marginal Function of Outlier Sum Statistic. 281-286
• 
  Chen Wang, Sook S. Ha, Yue Joseph Wang, Jianhua Xuan, Eric P. Hoffman: Computational Analysis of Muscular Dystrophy Sub-types Using a Novel Integrative Scheme. 287-292
• 
  Yajuan Wang, Carolyn Penstein Rosé, Antonio Ferreira, Dennis M. McNamara, Robert L. Kormos, James F. Antaki: A Classification Approach for Risk Prognosis of Patients on Mechanical Ventricular Assistance. 293-298
• 
  Guangzhi Qu, Hui Wu, Ishwar K. Sethi, Craig T. Hartrick: Neuropathic Pain Scale Based Clustering for Subgroup Analysis in Pain Medicine. 299-304

Machine Learning and Parallel Computing

• 
  Xavier Sierra-Canto, Francisco Madera-Ramirez, Victor Uc Cetina: Parallel Training of a Back-Propagation Neural Network Using CUDA. 307-312
• 
  Tsung-Kai Lin, Shao-Yi Chien: Support Vector Machines on GPU with Sparse Matrix Format. 313-318
• 
  David Pettinger, Giuseppe Di Fatta: Space Partitioning for Scalable K-Means. 319-324
• 
  Tapio Pahikkala, Antti Airola, Tapio Salakoski: Speeding Up Greedy Forward Selection for Regularized Least-Squares. 325-330

Applications I

• 
  Xiao-Wei Ai, Tianming Hu, Xi Li, Hui Xiong: Clustering High-frequency Stock Data for Trading Volatility Analysis. 333-338
• 
  Ulrich Weiss, Peter Biber, Stefan Laible, Karsten Bohlmann, Andreas Zell: Plant Species Classification Using a 3D LIDAR Sensor and Machine Learning. 339-345
• 
  Fabian Gieseke, Kai Lars Polsterer, Andreas Thom, Peter Zinn, Dominik Bomanns, Ralf-Jurgen Dettmar, Oliver Kramer, Jan Vahrenhold: Detecting Quasars in Large-Scale Astronomical Surveys. 352-357

Ensemble Learning

• 
  Jun Li, Dacheng Tao: Boosted Dynamic Cognitive Activity Recognition from Brain Images. 361-366
• 
  Jean Baptiste Faddoul, Boris Chidlovskii, Fabien Torre, Rémi Gilleron: Boosting Multi-Task Weak Learners with Applications to Textual and Social Data. 367-372
• 
  Xueyi Wang, Nicholas J. Davidson: The Upper and Lower Bounds of the Prediction Accuracies of Ensemble Methods for Binary Classification. 373-378
• 
  Amin Assareh, L. Gwenn Volkert, Joseph D. Ortiz: Evolutionary Selection of Regressional Predictors to Enhance the Performance of Microfossil-Based Paleotemperture Proxies. 379-385

Reinforcement Learning

• 
  Omkar J. Tilak, Snehasis Mukhopadhyay: Decentralized and Partially Decentralized Reinforcement Learning for Distributed Combinatorial Optimization Problems. 389-394
• 
  Sriraam Natarajan, Gautam Kunapuli, Kshitij Judah, Prasad Tadepalli, Kristian Kersting, Jude W. Shavlik: Multi-Agent Inverse Reinforcement Learning. 395-400
• 
  Alexander Hans, Steffen Udluft: Ensembles of Neural Networks for Robust Reinforcement Learning. 401-406
• 
  Mandana Hamidi, Amir Fijany, Jean-Guy Fontaine: Enhancing Inference in Relational Reinforcement Learning Via Truth Maintenance Systems. 407-413

Bayesian Learning

• 
  Mark Bartlett, Iain Bate, James Cussens: Learning Bayesian Networks for Improved Instruction Cache Analysis. 417-423
• 
  Theodoros Damoulas, Samuel Henry, Andrew Farnsworth, Michael Lanzone, Carla P. Gomes: Bayesian Classification of Flight Calls with a Novel Dynamic Time Warping Kernel. 424-429
• 
  Edimilson Batista dos Santos, Estevam R. Hruschka Jr., Nelson F. F. Ebecken: Evolutionary Algorithm Using Random Multi-point Crossover Operator for Learning Bayesian Network Structures. 430-435
• 
  Drausin Wulsin, Justin Blanco, Ram Mani, Brian Litt: Semi-Supervised Anomaly Detection for EEG Waveforms Using Deep Belief Nets. 436-441

Online and Incremental Learning

• 
  Kilian Förster, Samuel Monteleone, Alberto Calatroni, Daniel Roggen, Gerhard Tröster: Incremental kNN Classifier Exploiting Correct-Error Teacher for Activity Recognition. 445-450
• 
  Christophe Rodrigues, Pierre Gérard, Céline Rouveirol, Henry Soldano: Incremental Learning of Relational Action Rules. 451-458
• 
  Adam M. Sykulski, Niall M. Adams, Nicholas R. Jennings: On-Line Adaptation of Exploration in the One-Armed Bandit with Covariates Problem. 459-464
• 
  Anuraag Sridhar, Arcot Sowmya, Paul Compton: On-line, Incremental Learning for Real-Time Vision Based Movement Recognition. 465-470

Machine Learning Methods for Biomedical Literature Analysis and Text Retrieval

• 
  Neil Barrett, Jens H. Weber-Jahnke: Building a Biomedical Tokenizer Using the Token Lattice Design Pattern and the Adapted Viterbi Algorithm. 473-478
• 
  Xiaoli Zhang, Jie Zou, Daniel X. Le, George R. Thoma: A Structural SVM Approach for Reference Parsing. 479-484
• 
  Adrian Benton, Shawndra Hill, Lyle Ungar, Annie Chung, Charles E. Leonard, Cristin Freeman, John H. Holmes: A System for De-identifying Medical Message Board Text. 485-490
• 
  Won Kim, W. John Wilbur: Improving a Gold Standard: Treating Human Relevance Judgments of MEDLINE Document Pairs. 491-498
• 
  Lana Yeganova, Donald C. Comeau, W. John Wilbur: Identifying Abbreviation Definitions Machine Learning with Naturally Labeled Data. 499-505

Machine Learning in Bioinformatics and Computational Biology

• 
  Dianwei Han, Guiliang Tang, Jun Zhang: A Parallel Algorithm for Predicting the Secondary Structure of Polycistronic MicroRNAs. 509-514
• 
  Chunmei Liu, Hui Li, Alison Leonce, Legand L. Burge III, John Trimble, Peter Keiller, Abdul-Aziz Yakubu: A Heuristic Algorithm for Finding the Longest Pathways in a Biochemical Network. 515-522
• 
  Faisal Rezwan, Yi Sun, Neil Davey, Rod Adams, Alistair G. Rust, Mark Robinson: Using Randomised Vectors in Transcription Factor Binding Site Predictions. 523-527
• 
  Betty Yee Man Cheng, Jaime G. Carbonell: Automatic Detection of HIV Drug Resistance-Associated Mutations. 528-533
• 
  Boseon Byeon, Khaled Rasheed: Selection of Classifier and Feature Selection Method for Microarray Data. 534-539
• 
  Yue Fan, Mark A. Kon, Shinuk Kim, Charles DeLisi: Smoothing Gene Expression Using Biological Networks. 540-545
• 
  Nicholas J. Davidson, Xueyi Wang: Non-Alignment Features Based Enzyme/Non-Enzyme Classification Using an Ensemble Method. 546-551

Dynamic Learning in Non-Stationary Environments

• 
  Moamar Sayed Mouchaweh: Learning in Dynamic Environments: Application to the Identification of Hybrid Dynamic Systems. 555-560
• 
  Lin Zhang, Hongyu Li: Incremental Nyström Low-Rank Decomposition for Dynamic Learning. 561-566
• 
  Pierre Beauseroy, Andre Smolarz, Yuan Dong, Xiyan He: Dynamic Decision Method Based on Contextual Selection of Representation Subspaces. 567-572
• 
  Edwin Lughofer: On Dynamic Selection of the Most Informative Samples in Classification Problems. 573-579
• 
  Laurent Hartert, Moamar Sayed Mouchaweh: A Hybrid Multi-classifier to Characterize and Interpret Hemiparetic Patients Gait Coordination. 580-585
• 
  Abdullah Almaksour, Éric Anquetil: Improving Premise Structure in Evolving Takagi-Sugeno Neuro-Fuzzy Classifiers. 586-591

Autonomous Machine Learning

• 
  J. Javier Rainer, Ramón Galán: Learning to Be a Good Tour-Guide Robot. 595-600
• 
  Malik Tahir Hassan, Asim Karim, Fahad Javed, Naveed Arshad: Self-Optimizing a Clustering-based Tag Recommender for Social Bookmarking Systems. 601-606
• 
  Nistor Grozavu, Lazhar Labiod, Younès Bennani: Autonomous Clustering Characterization for Categorical Data. 607-613

Business Intelligent Applications and Technologies

• 
  John C. Handley, Marie-Luise Schneider, Victor Ciriza, Jeffrey Earl: Extreme Volume Detection for Managed Print Services. 617-622
• 
  Zhiguo Li, Gregory Kott: Predicting Remaining Useful Life Based on the Failure Time Data with Heavy-Tailed Behavior and User Usage Patterns Using Proportional Hazards Model. 623-628
• 
  David B. Bracewell: Semi-Automatic WordNet Based Emotion Dictionary Construction. 629-634
• 
  I-Hsien Yin, Estevam R. Hruschka Jr., Heloisa de Arruda Camargo: Intelligent Classification System Using a Pruned Bayes Fuzzy Rule Set. 635-640

Machine Learning with Multimedia Data

• 
  Sam Davies, Denise Bland: Interestingness Detection in Sports Audio Broadcasts. 643-648
• 
  Pedro Mercado, Hanna M. Lukashevich: Feature Selection in Clustering with Constraints: Application to Active Exploration of Music Collections. 649-654
• 
  Erik M. Schmidt, Youngmoo E. Kim: Prediction of Time-Varying Musical Mood Distributions Using Kalman Filtering. 655-660
• 
  Behrouz Saghafi, Deepu Rajan: Multi-view Clustering of Visual Words Using Canonical Correlation Analysis for Human Action Recognition. 661-666

Machine Learning in Energy Applications (I)

• 
  Omer Ozkan, Muharrem Aktas, Huseyin Serdar Kuyuk, Serkan Bayraktaroglu: Energy Production and Economic Growth: A Causality Analysis for Turkey Based on Computer. 669-674
• 
  Faa-Jeng Lin, Jonq-Chin Hwang, Kuang-Hsiung Tan, Zong-Han Lu, Yung-Ruei Chang: Control of Doubly-Fed Induction Generator System Using PIDNNs. 675-680
• 
  Huseyin Serdar Kuyuk, Omer Ozkan, Ramazan Kayikci, Serkan Bayraktaroglu: Modelling Turkey's Energy Consumption Based on Artificial Neural Network. 681-685
• 
  Umut Firat, Seref Naci Engin, Murat Saraclar, Aysin Baytan Ertuzun: Wind Speed Forecasting Based on Second Order Blind Identification and Autoregressive Model. 686-691
• 
  Ilhami Colak, Ramazan Bayindir, Orhan Kaplan, Ferhat Tas: DC Bus Voltage Regulation of an Active Power Filter Using a Fuzzy Logic Controller. 692-696

Machine Learning in Energy Applications (II)

• 
  Ramazan Bayindir, Alper Gorgun: Hardware Implementation of a Real-Time Neural Network Controller Set for Reactive Power Compensation Systems. 699-703
• 
  Antonietta Grasso, Jutta Willamowski, Victor Ciriza, Yves Hoppenot: The Personal Assessment Tool: A System Providing Environmental Feedback to Users of Shared Printers for Providing Environmental Feedback. 704-709
• 
  Halil-Ibrahim Bulbul, Özkan Ünsal: Determination of Vocational Fields with Machine Learning Algorithm. 710-713
• 
  Ilhami Colak, Seref Sagiroglu, Mehmet Demirtas, Hamdi Tolga Kahraman: Determining Suitability of Locations for Installation of Solar Power Station Based on Probabilistic Inference. 714-719
• 
  Fujio Kurokawa, Hidenori Maruta, Jun'ya Sakemi, Akihiro Nakamura, Hiroyuki Osuga: A New Prediction Based Digital Control DC-DC Converter. 720-725
• 
  Tina Yu: Modeling Occupancy Behavior for Energy Efficiency and Occupants Comfort Management in Intelligent Buildings. 726-731

Machine Learning Methods in Cancer and Radiation Therapy

• 
  Jung Hun Oh, Jeffrey Craft, Rawan Al-Lozi, Manushka Vaidya, Yifan Meng, Joseph O. Deasy, Jeffrey D. Bradley, Issam El-Naqa: Predicting Local Failure in Lung Cancer Using Bayesian Networks. 735-739
• 
  Florian Buettner, Sarah Gulliford, Steve Webb, Mike Partridge, Aisha B. Miah, Kevin J. Harrington, Christopher M. Nutting: Using a Bayesian Feature-selection Algorithm to Identify Dose-response Models Based on the Shape of the 3D Dose-distribution: An Example from a Head-and-neck Cancer Trial. 740-745
• 
  Mark Bangert, Philipp Hennig, Uwe Oelfke: Using an Infinite Von Mises-Fisher Mixture Model to Cluster Treatment Beam Directions in External Radiation Therapy. 746-751
• 
  Melanie Mitchell, James A. Tanyi, Arthur Y. Hung: Automatic Segmentation of the Prostate Using a Genetic Algorithm for Prostate Cancer Treatment Planning. 752-757
• 
  Sheng You, Esra Ataer Cansizoglu, Deniz Erdogmus, James Tanyi, Jayashree Kalpathy-Cramer: A Novel Application of Principal Surfaces to Segmentation in 4D-CT for Radiation Treatment Planning. 758-763

Poster Session

• 
  Sung Duck Lee, Duck-Ki Kim: Bayesian Inferences and Forecasting in Spatial Time Series Models. 767-770
• 
  Shobeir Fakhraei, Hamid Soltanian-Zadeh, Farshad Fotouhi, Kost V. Elisevich: Consensus Feature Ranking in Datasets with Missing Values. 771-775
• 
  Santhosh Pathical, Gürsel Serpen: Hybridization of Base Classifiers of Random Subsample Ensembles for Enhanced Performance in High Dimensional Feature Spaces. 776-781
• 
  Haimonti Dutta, David L. Waltz, Karthik M. Ramasamy, Philip Gross, Ansaf Salleb-Aouissi, Hatim Diab, Manoj Pooleery, Catherine A. Schevon, Ronald Emerson: Patient-Specific Seizure Detection from Intra-cranial EEG Using High Dimensional Clustering. 782-787
• 
  Valentina Bayer Zubek, Faisal M. Khan: Aggregating Multiple Biological Measurements Per Patient. 788-792
• 
  Rafael Augusto Ferreira do Carmo, Fabricio Gomes de Freitas, Jerffeson Teixeira de Souza: Empowering Simultaneous Feature and Instance Selection in Classification Problems through the Adaptation of Two Selection Algorithms. 793-796
• 
  Madhavi Vedula Ratnagiri, Lawrence R. Rabiner, Biing-Hwang Juang: Feature Transformation and Model Design Using Minimum Classification Error. 797-802
• 
  Man Chon U, Vasim Mahamuda, Khaled Rasheed: On the Scalability of Supervised Learners in Metagenomics. 803-807
• 
  Sai Venu Gopal Lolla, Lawrence L. Hoberock: On Estimation of Quantiles for Pairwise Distances. 808-812
• 
  Yarin Gal, Mireille Avigal: Overcoming Alpha-Beta Limitations Using Evolved Artificial Neural Networks. 813-818
• 
  Mónica Millán-Giraldo, Javier Salvador Sánchez, V. Javier Traver: A Comparison of Techniques for Handling Incomplete Input Data with a Focus on Attribute Relevance Influence. 819-822
• 
  Malak Alshawabkeh, Micha Moffie, Fatemeh Azmandian, Javed A. Aslam, Jennifer G. Dy, David R. Kaeli: Effective Virtual Machine Monitor Intrusion Detection Using Feature Selection on Highly Imbalanced Data. 823-827
• 
  Thashmee Karunaratne, Henrik Boström, Ulf Norinder: Pre-Processing Structured Data for Standard Machine Learning Algorithms by Supervised Graph Propositionalization - A Case Study with Medicinal Chemistry Datasets. 828-833
• 
  Nobuyuki Ohmori, Tatsunori Mori: Novel Approach for Test Methods Automatic Selection in Product Reliability: Improved Method for Acquiring Part-Whole Relation. 834-839
• 
  Sebastian Bohlmann, Volkhard Klinger, Helena Szczerbicka: System Identification with Multi-Agent-based Evolutionary Computation Using a Local Optimization Kernel. 840-845
• 
  Panganai Gomo, Mike Spann: Power Iteration Denoising. 846-850
• 
  Bartlomiej Sniezynski, Wojciech Wójcik, Jan D. Gehrke, Janusz Wojtusiak: Combining Rule Induction and Reinforcement Learning: An Agent-based Vehicle Routing. 851-856
• 
  Diego Uribe: Domain Adaptation in Sentiment Classification. 857-860
• 
  Jian Zhang: The Influence Machine: Nonnegative Instance-Space Learning with Differentiated Regularization. 861-866
• 
  Alexander M. Duda, Stephen E. Levinson: Nonlinear Dynamical Multi-Scale Model of Associative Memory. 867-872
• 
  Hanene Azzag, Mustapha Lebbah, Aymen Arfaoui: Map-TreeMaps: A New Approach for Hierarchical and Topological Clustering. 873-878
• 
  Alina Lazar: A Comparison of Linear Support Vector Machine Algorithms on Large Non-Sparse Datasets. 879-882
• 
  Thomas P. Karnowski, Itamar Arel, Derek Rose: Deep Spatiotemporal Feature Learning with Application to Image Classification. 883-888
• 
  Gonzalo A. Ruz, Eric Goles Ch.: Learning Gene Regulatory Networks with Predefined Attractors for Sequential Updating Schemes Using Simulated Annealing. 889-894
• 
  V. Suresh Kumar, S. Vijaya Chandra, C. Susil Kumar: Neuro-Fuzzy Function Approximations Using Feedforward Networks - An Application of Sigmoidal Signal. 895-898
• 
  Jan Outrata: Boolean Factor Analysis for Data Preprocessing in Machine Learning. 899-902
• 
  Gustavo E. A. P. A. Batista, Bilson J. L. Campana, Eamonn J. Keogh: Classification of Live Moths Combining Texture, Color and Shape Primitives. 903-906
• 
  Pedro Canotilho Ribeiro, Plinio Moreno, José Santos-Victor: Unsupervised and Online Update of Boosted Temporal Models: The UAL2Boost. 907-912
• 
  Soumi Ray, Tim Oates: Discovering and Characterizing Hidden Variables in Streaming Multivariate Time Series. 913-916
• 
  Akshay Vashist, Siun-Chuon Mau, Alexander Poylisher, Ritu Chadha, Abhrajit Ghosh: Predicting End-to-end Network Load. 917-920
• 
  Omar Arif, Patricio A. Vela, Wayne Daley: Pre-image Problem in Manifold Learning and Dimensional Reduction Methods. 921-924
• 
  Abid M. Malik: Spatial Based Feature Generation for Machine Learning Based Optimization Compilation. 925-930
• 
  Sohan Seth, Jose C. Principe: Variable Selection: A Statistical Dependence Perspective. 931-936
• 
  Juan Luo, Alexander Brodsky: An Optimal Regression Algorithm for Piecewise Functions Expressed as Object-Oriented Programs. 937-942
• 
  Jervis Pinto, Alan Fern, Tim Bauer, Martin Erwig: Robust Learning for Adaptive Programs by Leveraging Program Structure. 943-948
• 
  Man Chon U, Khaled Rasheed: A Relative Tendency Based Stock Market Prediction System. 949-953
• 
  Wei Peng: Constrained Nonnegative Tensor Factorization for Clustering. 954-957
• 
  Foaad Khosmood, Robert Levinson: Automatic Synonym and Phrase Replacement Show Promise for Style Transformation. 958-961

Posters from Special Sessions

• 
  Antonio Jimeno-Yepes, Alan R. Aronson: Query Expansion for UMLS Metathesaurus Disambiguation Based on Automatic Corpus Extraction. 965-968
• 
  Dazhuo Li, Fahim Mohammad, Eric C. Rouchka: A Bayesian Nonparametric Model for Joint Relation Integration and Domain Clustering. 969-974
• 
  Sonjia Waxmonsky, John Goldsmith, Andrey Rzhetsky: Discovering and Counting Biomedical Verbs. 975-978
• 
  Hui Li, Chunmei Liu, Xumin Liu, Macire Diakite, Legand L. Burge III, Abdul-Aziz Yakubu, William M. Southerland: Peptide Sequence Tag-Based Blind Identification-based SVM Model. 979-984
• 
  J. Kildea, M. Evans, W. Parker: A Framework for Comprehensive Electronic QA in Radiation Therapy. 985-988
• 
  Antti Airola, Tapio Pahikkala, Jorma Boberg, Tapio Salakoski: Applying Permutation Tests for Assessing the Statistical Significance of Wrapper Based Feature Selection. 989-994
• 
  Rezarta Islamaj Dogan, Aurélie Névéol, Zhiyong Lu: A Textual Representation Scheme for Identifying Clinical Relationships in Patient Records. 995-998
• 
  Liu Dan, Yue KaiDuan: The Design and Application of the Public Booking Service System. 999-1002

ICMLA Challenge Posters

• 
  Theodoros Giannakopoulos, Sergios Petridis: Unsupervised Speaker Clustering in a Linear Discriminant Subspace. 1005-1009
• 
  Ondej Sykora: Speaker Clustering Using Trails in Feature Space. 1010-1014
• 
  Jianfei Wu, Loai Al Nimer, Omar Al Azzam, Charith Chitraranjan, Saeed Salem, Anne M. Denton: Sequential Data Clustering. 1015-1020