ICMLA 2008 Conference Program

Embassy Suites Hotel La Jolla, 4550 La Jolla Village Drive, San Diego, CA USA

December 11 – 13, 2008


Dec. 10 (5:00 pm – 8:00 pm),  Dec. 11-13 (8:00 am – 5:00 pm)

Best Paper Award





Paper Title



Indulge East

A. Stuhlsatz, H.G. Meier, A. Wendemuth

Making the Lipschitz Classifier Practical via Semi-Infinite Programming

Best Poster Award



Indulge East

N. Jrad, E. Grall, P. Beauseroy

A supervised decision rule for multiclass problems minimizing a loss function




Dec. 11 Thursday


Opening Remarks


Invited Talk – Prof. Dan Roth, UIUC (Embassy Ballroom)

Constrained Conditional Models: Learning and Inference in Natural Language Understanding


Regular paper sessions (20 minutes each)


Session 1: Reinforcement Learning and Markov Process

Embassy Ballroom, Session Chair: Mustapha Lebbah

·   Basis Function Construction in Reinforcement Learning using Cascade-Correlation Learning Architecture. Sertan Girgin, Philippe Preux

·   A Predictive Model for Imitation Learning in Partially Observable Environments. Abdeslam Boularias

·   Classifier-Based Policy Representation. Ioannis Rexakis, Michail Lagoudakis

·   Prediction-directed Compression of POMDPs. Abdeslam Boularias

·   Empirical Comparison of Greedy Strategies for Learning Markov Networks of Treewidth k. Kyle Nunez, Jianhua Chen, Peter Chen, Robert Lax, Guoli Ding, Brian Marx

Session 2: Unsupervised Methods

Indulge East, Session Chair: Steve Jiang

·   Model Based Unsupervised Learning Guided by Abundant Background Samples. Eric Rouchka, Rami Mahdi

·   X-Sim: A new Similarity Measure for the co-clustering task. Fawad Hussain, Gilles Bisson

·   Relational Analysis for Consensus Clustering from Multiple Partitions. Hamid Benhadda, Mustapha Lebbah, Younes Bennani

·   Probabilistic Mixed Topological Map for Categorical and Continuous Data. Nicoleta Rogovschi, Mustapha Lebbah, Younes Bennani

·   Farthest Centroids Divisive Clustering. Yousef Saad, Haw-ren Fang


Dec. 11 Thursday


Coffee Break


Regular paper sessions (20 minutes each)


Session 1: Statistical Learning

Embassy Ballroom, Session Chair: Daniela Mayum Ushizima

·   Prioritizing Health Promotion Plans with k-Bayesian Network Classifier. Ken Ueno, Toshio Hayashi, Koichiro Iwata, Nobuyoshi Honda, Youichi Kitahara, Topon Paul

·   Temporal Exemplar-based Bayesian Networks for Facial Expression Recognition. Lifeng Shang, Kwok-Ping Chan

·   An Investigation of Non-Uniform Error Cost Function Design in Automatic Speech Recognition. Qiang Fu, Bing Hwang Juang

·   Graph-based Multilevel Dimensionality Reduction with Applications to Eigenfaces and Latent Semantic Indexing. Sofia Sakellaridi, Haw-ren Fang, Yousef Saad

Session 2: Ensemble-based Methods

Indulge East, Session Chair: Scott Smith

·   A Greedy Approach for Building Classification Cascades. Sherif Abdelazeem

·   Calibrating Random Forests. Henrik Bostrom

·   Decision Tree Ensemble: Small Heterogeneous Is Better Than Large Homogeneous. Mike Gashler, Christophe Giraud-Carrier, Tony Martinez

·   On the Use of Accuracy and Diversity Measures for Evaluating and Selecting Classifier Ensembles. Tuve Lofstrom, Ulf Johansson, Henrik Bostrom


Lunch Break


Invited Talk – Prof. Jude Shavlik, Univ. of Wisconsin (Embassy Ballroom)

Machine Learning via Advice Taking


Regular paper sessions (20 minutes each)


Session 1: Evolutionary Algorithms and Genetic Programming

La Jolla Room, Session Chair: Dechang Chen

·   Comparison with Parametric Optimization in Credit Card Fraud Detection. Manoel Gadi, Alair Do Lago, Xidi Wang

·   A Study of GP’s Division Operators for Symbolic Regression. Matej Sprogar

·   Adaptively Evolving Probabilities of Genetic Operators. Fatemeh Vafaee, Peter Nelson, Chi Zhou, Weimin Xiao

·   Scalable Patch Management using Evolutionary Analysis of Attack Graphs. Melissa Danforth

Session 2: Inductive Learning

Indulge East, Session Chair: Kwok-ping Chan

·   New Insights Into Learning Algorithms and Datasets. Jun won Lee, Christophe Giraud-Carrier

·   Learning Analysis by Reduction from Positive Data Using Reversible Languages. Petr Hoffmann

·   Comparison of Four Performance Metrics for Evaluating Sampling Techniques for Low Quality Class-Imbalanced Data. Taghi Khoshgoftaar, Andres Folleco, Amri Napolitano



Coffee Break





Dec. 11 Thursday


Regular paper sessions (20 minutes each)


Session 1: Machine Learning Applications I

La Jolla Room, Session Chair: Issam El Naqa

·   Combining the Wavelet Transform and Forecasting Models to Predict Gas Forward Prices. Hang Nguyen, Ian Nabney

·   Improving Accuracy in the Montgomery County Corrections Program Using Case-Based Reasoning. Caio Soares, Christin Hamilton, Lacey Montgomery, Juan Gilbert

·   Extraction of Failure Graphs from Structured and Unstructured data. Martin Schierle, Daniel Trabold

·   Adaptive Localization Through Transfer Learning in Indoor Wi-Fi Environment. Zhuo Sun, Yiqiang Chen, Juan Qi, Junfa Liu

Session 2: Machine Learning Applications II

Indulge East, Session Chair: Zhenqiu Liu

·   Does Wikipedia Information Help Netflix Predictions? Fraser Anderson, John Lees-Miller, Bret Hoehn, Russell Greiner

·   Distributed Optimization Strategies for Mining on Peer-to-Peer Networks. Haimonti Dutta, Ananda Mathur

·   Adaptive Control of Antilock Braking System Using Grey Multilayer Feedforward Neural Networks. Erdal Kayacan, Yesim Oniz, Okyay Kaynak, Andon Topalov

·   A Fully Automatic Crossword Generator. Michelangelo Diligenti, Leonardo Rigutini, Marco Maggin, Marco Gori


Poster Session I: Accepted Papers with odd ID numbers (Embassy Ballroom)


























Dec. 12, Friday


Invited Talk – Prof. Michael Jordan, UC Berkeley (Embassy Ballroom)

Applied Nonparametric Bayes


Tutorial Sessions

9:30 – 12:30

Tutorial Session 1: (Embassy Ballroom)

Prof. Bin Yu, UC Berkeley

Information Theory and Statistics

Tutorial Session 2: (Indulge East)

Prof. Christophe Giraud-Carrier, Brigham Young University


Tutorial Session 3: (La Jolla Room)

Prof. Brian Potetz, Univ. of Kansas

Graphical Models for Computer Vision

11:00 – 11:15

Coffee Break


Lunch Break


Regular paper sessions (20 minutes each)


Session 1: Machine Learning Applications III

La Jolla Room, Session Chair: M. Arif Wani

·   Boltzmann Machine Topology Learning for Distributed Sensor Networks Using Loopy Belief Propagation Inference. Luigi Cambrini, Cristina Picus, Wolfgang Herzner

·   CoReJava: Learning Functions Expressed as Objected-Oriented Programs. Juan Luo, Alex Brodsky, Hadon Nash

·   Speaker Siglet Detection for Business Microscope. Jun Nishimura, Nobuo Sato, Tadahiro Kuroda

·   Automated analysis for detecting beams in laser wakefield simulations. Daniela Ushizima, Oliver Rubel, Prabhat, Gunther Weber, Edward Bethel, Cecilia Aragon, Cameron Gueddes, Estelle Cormier-Michael, Bernd Hamann, Peter Messmer, Hans Hagen

·   Mapping Uncharted Waters: Exploratory Analysis, Visualization, and Clustering of Oceanographic Data. Joshua Lewis, Pincelli Hull, Kilian Weinberger, Lawrence Saul

Session 2: Machine Learning Applications IV

Indulge East, Session Chair: Silvia Chiappa

·   Distributed Planning in Stochastic Games with Communication. Andriy Burkov, Brahim Chaib-draa

·   Multi-stage Learning of Linear Algebra Algorithms. Victor Eijkhout, Erika Fuentes

·   Optimization on a Novel Quantum Greedy Approach based on Learning Strategy for Zero and One Knapsack Problem and Evaluation. Mir Shahriar Emami

·   Semi-supervised IFA with prior knowledge on the mixing process. An application to a railway device diagnosis. Latifa Oukhellou, Etienne Come, Zohra Leila Cherfi, Patrice Aknin

·   Force feature spaces for visualization and classification. Dragana Veljkovic, Kay Robbins




Dec. 12, Friday


Invited Talk – Prof. Bin Yu, UC Berkeley (Embassy Ballroom)

Seeking Interpretable Models for High-Dimensional Data


Coffee Break


Regular paper sessions (20 minutes each)


Session 1: Image Processing and Applications

La Jolla Room, Session Chair: Thomas Villmann

·   Detection of Unnatural Movement Using Epitomic Analysis.  Wooyoung Kim, James Rehg

·   Image Segmentation as Learning on Hypergraphs. Lei Ding, Alper Yilmaz

·   Autonomous detector using saliency map model and modified mean-shift tracking for a blind spot monitor in a car. Sungmoon Jeong, Sang-Woo Ban, Minho Lee

·   Probabilistic Exploitation of the Lucas and Kanade Smoothness Constraint. Volker Willert, Julian Eggert, Marc Toussaint, Edgar Komer

·   Video Shot Segmentation Using Late Fusion Technique. Krishna Mohan Chalavadi, Surathkal Dhananjaya, Madras Yegnanarayana

Session 2: Learning and Applications

Embassy Ballroom, Session Chair: Leif Peterson

·   Learning-based Fusion for Data Deduplication. Jared Dinerstein, Sabra Dinerstein, Parris Egbert, Stephen Clyde

·   Target Selection: A New Learning Paradigm and its Application to Genetic Association Studies. Johannes Mohr, Sambu Seo, Imke Puls, Andreas Heinz, Klaus Obermayer

·   Are Neural Fields Suitable for Vector Quantization? Lucian Alecu, Herve Frezza-Buet

·   Estimation of Gaussian Mixtures from Moments. John Byrnes

Simultaneously Removing Noise and Selecting Relevant Features for High Dimensional Noisy Data. Boseon Byeon, Khaled Rasheed


Poster Session II: Accepted Papers with Even ID numbers (La Jolla Room)


Banquet (Embassy Ballroom)
















Dec. 13, Saturday


Invited Talk – Andrew Moore, Google Pittsburgh (Embassy Ballroom)

Understanding the Sky: Asteroid tracking and Google Sky


Regular paper sessions (20 minutes each)


Session 1: Statistical Learning

Embassy Ballroom, Session Chair: Sun Kim

·   Improving Kernel Density Classifier using Corrective Bandwidth Learning with Smooth Error Loss Function. Dwi Sianto Mansjur, Bing Hwang Juang

·   Boundary Constrained Manifold Unfolding. Bo Liu, Hongbin Zhang, Wenan Chen

·   Fast and Regularized Minimum Volume Ellipsoid Metric for Query–based Learning. Karim Abou-Moustafa, Frank Ferrie

·   A Bayesian Approach to Switching Linear Gaussian State-Space Models for Unsupervised Time-Series Segmentation. Silvia Chiappa

·   A Bayesian Learning Automaton for Solving Two-Armed Bernoulli Bandit Problems. Ole-Christoffer Granmo

Session 2: Support Vector Machines

Indulge East, Session Chair: Peter Bajcsy

·   Combining dissimilarities in a Hyper Reproducing Kernel Hilbert Space for Complex Human Cancer Prediction. Angela Blanco, Manuel Martin-Merino, Javier De Las Rivas

·   Making the Lipschitz Classifier Practical via Semi-Infinite Programming. Andre Stuhlsatz, Hans-Gunter Meier, Andreas Wendemuth

·   A supervised decision rule for multiclass problems minimizing a loss function. Nisrine Jrad, Edith Grall, Pierre Beauseroy

·   Selecting Examples in Manifold Reduced Feature Space for Active Learning. Catarina Silva, Bernardete Ribeiro

·   Network-constrained Support Vector Machine for Classification. Li Chen, Jianhua Xuan, Yue Wang, Rebecca B. Riggins, Robert Clarke

Special Session 3: Machine Learning Applications in Radiotherapy

La Jolla Room, Session Chair: Steve Jiang and Martin Murphy

·   Towards on-line treatment verification using cine EPID for hypofractionated lung radiotherapy. Xiaoli Tang, Tong Lin, Steve Jiang

·   Segmentation of Lesions with Improved Specificity in Computer-Aided Diagnosis Using a Massive-Training Artificial. Kenji Suzuki

·   Using Neural Networks to Predict Lung Tumor Motion during Radiation Therapy. Martin Murphy


Coffee Break








Dec. 13, Saturday


Special Sessions papers (15 minutes each)


Special Session 1: From Biological Intelligence to Machine Intelligence (I)

Embassy Ballroom, Session Chair: Wei Li

·   Simulation Experiments with Bio-Inspired Algorithms for Odor Source. Thomas Lochmatter, Alcherio Martinoli

·   Integration of Chemical and Visual Sensors for Identifying an Odor Source in Near Shore Ocean Conditions. Wei Li

·   Extraction of Meaningful Rules in a Medical Database. Sang Suh

·   Dimension Reduction via Unsupervised Learning Yields Significant Computational Improvements for Support Vector Machine Based Protein Family Classification. Bobbie-Jo Webb-Robertson, Melissa Matzke, Christopher Oehmen

Special Session 2: Machine Learning and Data Mining Methods in Bioinformatics

Indulge East, Session Chair: Zhenqiu Liu

·   Comprehensible Models for Predicting Molecular Interaction with Heart-Regulating Genes. Cecilia Sonstrod, Ulf Johansson, Ulf Norinder, Henrik Bostrom

·   RNA Search Acceleration with Genetic Algorithm Generated Decision Trees. Scott Smith

·   Detection of Sequential Outliers using a Variable Length Markov Model. Cecile Low-Kam, Anne Laurent, Maguelonne Teisseire

·   Inferring Sparse Kernel Combinations and Relevance Vectors: An application to subcellular localization of proteins. Theodoros Damoulas, Mark Girolami, Yiming Ying, Colin Campbell

Special Session 3: Machine Learning Applications

La Jolla Room, Session Chair: Mark Kon

·   Research on Data Mining Algorithms for Automotive Customers’ Behavior Prediction Proble. Lan Huang, Chunguang Zhou, Yuqin Zhou, Zhe Wang

·   Dynamic Knowledge Management Procedure Using Fuzzy Clustering. Chingwei Chang, Kwoting Fang

·   Multimodal Music Mood Classification using Audio and Lyrics. Jens Grivolla, Cyril Laurier, Perfecto Herrera

·   A User Modeling Approach to Web Based Adaptive Educational Hypermedia Systems. Ilhami Colak, Seref Sagiroglu, Hamdi Kahraman


Lunch Break


Invited Talk – Prof. Philip Bourne, UC San Diego (Embassy Ballroom)

Machine Learning in the New World of Scholarly Communication


Special Session/ Workshop Papers (15 minutes each)


Special Session 1: From Biological Intelligence to Machine Intelligence (II)

Embassy Ballroom, Session Chair: Wei Li

·   A Qualia Framework for Ladar 3D Object Classification. Matthew Eyster, Michael Mendenhall, Steven Rogers

·   Development of CPT_M3D for Multiple Chemical Plume Tracing and Source Identification. Joseph Sutton, Wei Li

·   Active Stereo Olfactory Sensing System for Localization of Gas/Odor Source. Atsushi Kohnotoh, Hiroshi Ishida

·   Crayfish Robot Employing Chemical Information Flow to Locate a Chemical Source. Mari Ohashi, Yuichi Minagawa, Yuki Myoren, Hiroshi Ishida

·   Volatile marks for robotics guidance. Pedro Vieira Sousa, Lino Marques, Anibal Almeida

Workshop on Machine Learning in Bioinformatics and Biomedicine

Indulge East, Session Chair: Sun Kim

·   Prediction of Inter-residue Contact Clusters from Hydrophobic Cores. Peng Chen

·   Ensemble Machine Methods for DNA Binding. Mark Kon, Yue Fan, Charles Delisi

·   Gene Network Learning Using Regulated Dynamic Bayesian Network Methods. Xiaotong Lin, Xue-wen Chen

·   A Clustering Approach in Developing Prognostic Systems of Cancer Patients. Dechang Chen, Kai Xing, Donald Henson, Li Sheng, Arnold M. Schwartz, Xiuzhen Cheng   

·   Text, Image and Vector Graphics Based Appraisal of Contemporary Documents. Sang-Chul Lee, William McFadden, Peter Bajcsy

Special Session 2: Machine Learning Applications in Radiotherapy

La Jolla Room, Session Chair: Steve Jiang and Martin Murphy

·   Tumor Targeting for Lung Cancer Radiotherapy Using Machine Learning Techniques. Tong Lin, Laura Cervino, Xiaoli Tang, Nuno Vasconcelos, Steve Jiang

·   Nonlinear Kernel-based Approaches for Predicting Normal Tissue Toxicities. Issam El Naqa

·   Decision Fusion of Machine Learning Models to Predict Radiotherapy-induced Lung Pneumonitis. Shiva Das, Shifeng Chen, Joseph Deasy, Sumin Zhou, Fang-Fang Yin, Lawrence Marks


Coffee Break


Special Session 1: Application of Machine Learning in Constructing Biopatterns and Analysing Bioprofiles (3:40 pm – 5:40 pm)

La Jolla Room, Session Chair: Leif Peterson and Paulo Lisboa

·   External Validation of a Bayesian Neural Network Model in Survival Analysis. Azzam Taktak, Min Aung, Paulo Lisboa, Laurence Desjardins, Bertil Damato

·   Classification, Dimensionality Reduction, and Maximally Discriminatory Visualization of a Multicentre 1H-MRS Database of Brain Tumors. Paulo Lisboa, Enrique Romero, Alfredo Vellido, Margarida Julio-Sape, Carles Arus

·   Support Vector Machines versus Decision Templates in Biomarker Decision Fusion. Ioannis Dimou, Michalis Zervakis

·   Clustering by Affinity Propagation of breast carcinoma using biomarkers. Federico Ambrogi, Elena Raimondi, Patrizia Boracchi, Elia Biganzoli

·   A Comparison of Three Different Methods for Classification of Breast Cancer Data. Daniele Soria, Jonathan Garibaldi, Elia Biganzoli, Ian Ellis

·   Distance Metric Learning and Support Vector Machines for Classification of Mass Spectrometry Proteomics Data. Qingzhong Liu, Andrew Sung, Mengyu Qiao

·   Hermite/Laguerre Neural Networks for Classification of Artificial Fingerprints from Optical Coherence Tomography. Leif Peterson, Kirill Larin

·   Missing data imputation in longitudinal cohort studies. Paulo Lisboa, Ana Fernandes, Ian Jarman, Terence Etchells, Jose Fonseca, Elia Biganzoli, Chris Bajdik

Special Session 2: Machine Learning for Informatics (3:30 pm – 5:00 pm)

Embassy Ballroom, Session Chair: Seref Sagiroglu

·   Group testing in the development of an expanded cancer staging system. Dechang Chen, Kai Xing, Donald Henson, Li Sheng

·   Clustering for DNA Microarray Data analysis with a Graph Cut Based Algorithm. Chunmei Liu

·   Detecting disease associated Genes and gene-gene interactions with penalized AUC maximization. Zhenqiu Liu

·   Highly Scalable SVM Modeling with Random Granulation for Spam Sender Detection. Yuchun Tang, Yuanchen He, Sven Krasser

·   On the use of decision trees as behavioral approaches in intrusion detection. Karim Tabia

·   Video Steganalysis Based on the Expanded Markov and Joint Distribution on the Transform Domains. Qingzhong Liu, Andrew Sung, Mengyu Qiao

Special Session 3: Applications of Machine Learning in Medicine and Biology (3:45 pm – 5:00 pm)

Indulge East, Session Chair: Zhenqiu Liu

·   Assessing Torso Deformity in Scoliosis using Self-Organizing Neural Networks (SNN). Philip Igwe, Mahdieh Emrant, Samer Adeeb, Doug Hill

·   Automated Microarray Classification Based on P-SVM Gene Selection. Sambu Seo, Johannes Mohr, Klaus Obermayer

·   Microarray classification from several two-gene expression comparisons. Donald Geman, Bahman Afsari, Aik Choon Tan, Daniel Naiman

·   Automating Microarray Classification using General Regression Neural Networks.

Caio Soares, Lacey Montgomery, Kenneth Rouse, Juan Gilbert

·   Incremental Hybrid Approach for Microarray Classification. Arif Wani



Award Presentation (best papers, best posters, ICMLA competition award etc.) – La Jolla Room

Closing Remarks








Short Papers for Poster Session I:

·         A Combination of Positive and Negative Fuzzy Rules for Image Classification Problem. T. Nguyen, J. Wu

·         Incremental Learning for Multitask Pattern Recognition Problems. S. Ozawa, A. Roy

·         An improved Generalized Discriminant Analysis for Large-scale data set. W. Shi, Y. Guo, C. Jin, X. Xue

·         A Comparative Study of Selected Classification Accuracy in User Profiling. A. Cufoglu, M. Lohi, K. Madani

·         Estimation of Exercise Energy Expenditure Using a Wrist-Worn Accelerometer: a Linear Mixed Model Approach with Fixed-Effect Variable Selection. E. Haapalainen, P. Laurinen, J. Roning, H. Kinnunen

·         Improving Algorithm Accuracy Prediction with a Small Set of Effective Meta-Features. J. W. Lee, C. Giraud-Carrier

·         Machine Learning Techniques Applied to Dynamic Video Adapting Problem. R. Eisinger, R. Goularte, R. Romero

·         Comparative analysis of the impact of discretization on the classification with Naive Bayes and semi-Naďve Bayes classifiers. M. Mizianty, L. Kurgan, M. Ogiela

·         Handling large volumes of mined knowledge with a self-reconfigurable topology on distributed systems. N. A. L. Khac, L. M. Aouad, M. Kechadi

·         Dynamic modeling with Ensemble Kalman Filter trained recurrent neural networks. D. Mirikitani, N. Nikolaev

·         Entropy-Based Defuzzification Method with Experts’ Epistemic Uncertainty for Deteriorating Repairable Systems. C. Chang

·         Online Writer-independent Character Recognition Using a Novel Relational Context Representation. S. Izadi

·         Mining of Frequent Externally Extensible Outerplanar Graph Patterns. H. Yamasaki, T. Shoudai

·         A Multi-Expert Classification Framework with Transferable Voting for Intrusion Detection. T. P. Tran

·         Protein-Protein Interaction Prediction Using Single Class SVM. H. Lei

·         Statistical face recognition via a k-means iterative algorithm. A. Majumdar

·         Nonlinear discrete-time adaptive controller based on fuzzy rules emulated network and its estimated gradient. C. Treesatayapun


Short Papers for Poster Session II:

·         Efficient Hiding of Collaborative Recommendation Association Rules with Updates. S. L. Wang, T. Z. Lai, T. P. Hong, Y. L. Wu

·         Estimating Missing Data and Determining the Confidence of the Estimate Data. J. Mistry

·         A New Neural Network to Process Missing Data without Imputation. M. Randolph-Gips

·         Text classification using tree kernels and linguistic information. T. Gonoalves, P. Quaresma

·         Knowledge-Supervised Learning by Co-clustering Based Approach. C. Zhang

·         Comparison of Evaluation Metrics in Classification Applications with Imbalanced Datasets. M. fatourechi, R. Ward, S. Mason, J. Huggins, A. Schlogl, G. Birch

·         Comparison of the Effects of Morphological and Ontological Information on Text Categorization. C. Koirala, K. Rasheed

·         Applying Learning by Tutelage and Multimodal Interface to Sociable Robots. C. Policastro, G. Pais, V. Munhoz, R. Romero, G. Zuliani, E. Pizzolato

·         A Weighted Distance Measure for Calculating the Similarity of Sparsely Distributed Trajectories. P. Siirtola, P. Laurinen, J. Roning

·         An Application of Latent Dirichlet Allocation to Analyzing Software Evolution. E. Linstead, C. Lopes, P. Baldi

·         Biological Plausibility in Artificial Neural Networks: An Improvement on Earlier Models. A. Silva, J. L. Rosa

·         Research of PU Text Semi-Supervised Classification Based on Ontology Feature Extraction. N. Luo, W. Zuo, F. Yuan

·         Identification of patterns via region-growing parallel SOM neural network. I. Valova, D. MacLean, D. Beaton

·         Speech Emotion Recognition Using Canonical Correlation Analysis and Probabilistic Neural Network. L. Cen, W. Ser, Z. Yu

·         Data Integration for Recommendation Systems. Z. Xia, H. Qi, M. Tu, W. Zhang,

·         A Data Driven Knowledge Acquisition Method and Its Application in Power System Dynamic Stability Assessment. G. Lin, T. W. Wang, Y. Zhang, L. Zhang