Selected Publications

Most of the papers are copyright-protected and are provided here for viewing purposes only.

 2009

• P. Neskovic, I. Sherman, L. Wu, and L. N. Cooper. Learning faces with the BIAS model: on the importance of the sizes and locations of fixation regions, Neurocomputing, 27, 2915-2922, 2009.

2008

• L. Wu, P. Neskovic, and L. N. Cooper. A probabilistic model for classifying segmented images, ICPR 2008, in press.

• H. Ren, L. Wu, P. Neskovic, and L. N. Cooper. Approximating a non-homogenous HMM with dynamic spatial Dirichlet process, ICPR 2008, in press.

 2007

• L. Wu and P. Neskovic. Classifying EEG data into different memory loads across subjects, Lecture Notes In Computer Science: Artificial Neural Networks - ICANN, Vol.4669, pp. 149-158, 2007.

• L.. Wu, P. Neskovic and Luiz Pessoa. Dirichlet Process Mixture Model with Spatial Constraints. Technical Report IBNS-TR-2007-02, Brown University, 2007.

• L. Wu and P. Neskovic. A self-improving procedure for Bayes classification with few training examples. Technical Report IBNS-TR- 2007-01, Brown University, 2007.(updated version is here)

• L. Wu and P. Neskovic. Feature extraction for EEG classification: representing electrode outputs as a Markov stochastic process, ESANN, pp. 567-572, 2007.

• L. Wu, P. Neskovic, E. Reyes, E. Festa, and W. Heindel, Classifying n-back EEG data using entropy and mutual information features, ESANN, pp. 61-66, 2007. 

• P. Neskovic, Ian Sherman, L. Wu, and L. N. Cooper. How Important are the Sizes and Locations of the Fixation Regions for the BIAS Model? Natural Computation, Vol. 2, pp. 17-21, 2007.

• J. Wang, P. Neskovic, and L. N. Cooper. Bayes Classification Based on Minimum Bounding Spheres. Neurocomputing, Vol. 70, pp. 801-808, 2007.

• J. Wang, P. Neskovic, and L. N. Cooper. Improving Nearest Neighbor Rule with a Simple Adaptive Distance Measure. Pattern Recognition Letters, 28(2), pp. 207-213, 2007.

• J. Wang, P. Neskovic, and L. N. Cooper. Selecting Data for Fast Support Vector Machine Training. Studies in Computational Intelligence, Vol. 35, pp. 61-84, 2007.

        2006

• P. Neskovic, L. Wu, and L. N. Cooper. Learning by Integrating Information Within and Across Fixations. Lecture Notes In Computer Science: Artificial Neural Networks - ICANN, Vol. 4132, pp. 488-497, 2006.

• J. Wang, P. Neskovic, and L. N. Cooper. A minimum Sphere Covering Approach to Pattern Classification. ICPR, pp. 433-436, 2006.

• J. Wang, P. Neskovic, and L. N. Cooper.  Neighborhood Size Selection in the k-Nearest Neighbor Rule Using Statistical Confidence. Pattern Recognition, 39(3), pp. 417-423, 2006.

• L. Wu, P. Neskovic, and L. N. Cooper. Biologically Inspired Hierarchical Model for Feature Extraction and Localization. ICPR, pp. 259-262, 2006.

• J. Wang, P. Neskovic, and L. N. Cooper. Improving Nearest Neighbor Rule with a Simple Adaptive Distance Measure. Lecture Notes In Computer Science: Advances in Natural Computation - ICNC, Vol. 4221, pp. 43-46, 2006.

• L. Wu, P. Neskovic, and L. N. Cooper. Biologically Inspired Bayes Learning and its Dependence on the Distribution of the Receptive Fields. Lecture Notes In Computer Science: Advances in Natural Computation - ICNC, Vol. 4221, pp. 279-288, 2006.

• J. Wang, P. Neskovic, and L. N. Cooper. A Minimum Sphere Covering Approach to Learning. IBNS Technical Report 2006-03, Brown University, 2006.

• J. Wang, P. Neskovic, and L. N. Cooper. Learning Class Regions by Sphere Covering. IBNS Technical Report 2006-02, Brown University, 2006.

• P. Neskovic, L. Wu, and L. N. Cooper. Biologically Inspired Bayesian Approach for Learning Object Categories From Few Training Examples. IBNS Technical Report 2006-01, Brown University, 2006.

        2005

• T. Steinherz, E. Rivlin, N. Intrator, and P. Neskovic. An Integration of Online and Pseudo-Online Information for Cursive Word Recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence PAMI, 27(5), pp. 669-684, 2005.

• J. Wang, P. Neskovic, and L. N. Cooper. Pattern classification via single spheres. Lecture Notes in Computer Science: Discovery Science (DS), A. Hoffmann, H. Motoda, and T. Scheffer (Eds.), Springer-Verlag, Vol. 3735. pp. 241-252 , 2005.

• J. Wang, P. Neskovic, and L. N. Cooper.  A Statistical Confidence-Based Adaptive Nearest Neighbor Algorithm for Pattern Classification. Lecture Notes in Computer Science: Advances in Machine Learning and Cybernetics (ICMLC), Vol. 3930, pp. 548-557, 2005.

• P. Neskovic and L. N. Cooper. Visual Search for Object Features. Lecture Notes In Computer Science: Advances in Natural Computation (ICNC), L. Wang, K. Chen, Y. S. Ong (Eds.), Vol. 3610, pp. 877-887, 2005.

• J. Wang, P. Neskovic, and L. N. Cooper. Training Data Selection for Support Vector Machines. Lecture Notes In Computer Science: Advances in Natural Computation (ICNC), L. Wang, K. Chen, Y. S. Ong (Eds.), Vol. 3610, pp. 554-564, 2005.

• J. Wang, P. Neskovic, and L. N. Cooper. Locally Determining the Number of Neighbors in the k-Nearest Neighbor Rule Based on Statistical Confidence, Lecture Notes In Computer Science: Advances in Natural Computation (ICNC), L. Wang, K. Chen, Y. S. Ong (Eds.),  Vol. 3610, pp. 71-80, 2005.

• J. Wang, P. Neskovic, and L. N. Cooper. Pattern classification based on minimum bounding spheres. International Conference on Intelligent Computing (ICIC), pp. 1969 - 1978, 2005.

• J. Wang, P. Neskovic, and L. N. Cooper. A Probabilistic Model For Cursive Handwriting Recognition Using Spatial Context. ICASSP, Vol. 5, pp. 201-204, 2005.

        2000 - 2004

• J. Wang, P. Neskovic, and L. N. Cooper. Context-based Tracking of Object Features. IJCNN, Vol. 3, Pages 1775-1779, 2004.

• P. Neskovic, D. Schuster, and L. N Cooper. Biologically inspired recognition system for car detection from real-time video streams. Neural Information Processing: Research and Development, J. C. Rajapakse and L. Wang (eds.), Springer-Verlag, pp. 320-334, 2003.

• P. Neskovic and L. N. Cooper. Providing context for edge extraction in application to detection of cars from video streams. International Conference on Engineering Applications of Neural Networks, pp. 222-229, 2003.

• J. Wang, P. Neskovic, and L. N. Cooper. Partitioning a feature space using a locally defined confidence measure, ICANN/ICONIP, pp. 200-203, 2003.

• P. Neskovic and L. N. Cooper. Using Contextual Information To Selectively Adjust Preprocessing Parameters. Lecture Notes in Computer Science: Bio-Inspired Applications of Connectionism (IWANN), J. Mira, A. Prieto (Eds.), vol. 2085, p. 696-703, 2001.

• P. Neskovic and L. N. Cooper. Object segmentation using an array of interconnected neural networks with local receptive fields. IJCNN, pp. 1983-1989, 2001.

• P. Neskovic, P. C. Davis, and L. N. Cooper. Interactive Parts Model: an Application to Recognition of On-line Cursive Script. Advances in Neural Information Processing Systems (NIPS), pp. 974-980. 2000.

• P. Neskovic and L. N. Cooper. Neural Network Based Context Driven Recognition of On-line Cursive Script. IWFHR, pp. 352-362, 2000.

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