Neskovic, Predrag - Department of Physics & Institute for Brain and Neural Systems, Brown University

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Neskovic, Predrag - Department of Physics & Institute for Brain and Neural Systems, Brown University

Providing context for edge extraction in the application of car detection from video streams
Institute for Brain and Neural Systems Brown University
Biologically inspired recognition system for car detection from real-time video streams
Learning by Integrating Information Within and Across Fixations
Pattern Recognition 39 (2006) 417423 www.elsevier.com/locate/patcog
Training Data Selection for Support Vector Jigang Wang, Predrag Neskovic, and Leon N Cooper
Feature extraction for EEG classification: representing electrode outputs as a Markov
Classifying n-back EEG data using entropy and mutual information features
Context-based Tracking of Object Features Jigang Wang, Predrag Neskovic and Leon N Cooper
Locally Determining the Number of Neighbors in the k-Nearest Neighbor Rule Based on
Institute for Brain and Neural Systems Brown University
A PROBABILISTIC MODEL FOR CURSIVE HANDWRITING RECOGNITION USING SPATIAL CONTEXT
Learning faces with the BIAS model: On the importance of the sizes and locations of fixation regions
Approximating a non-homogeneous HMM with Dynamic Spatial Dirichlet Software Engineering College, Chongqing University Chongqing 400044, PR China
Classifying EEG data into different memory loads across subjects
Institute for Brain and Neural Systems Brown University
Institute for Brain and Neural Systems Brown University
Neurocomputing 70 (2007) 801808 Bayes classification based on minimum bounding spheres
Improving nearest neighbor rule with a simple adaptive distance measure
A Minimum Sphere Covering Approach to Pattern Classification Jigang Wang, Predrag Neskovic, Leon N Cooper
Institute for Brain and Neural Systems Brown University
Pattern Classification Based on Minimum Bounding Spheres
Partitioning a feature space using a locally defined confidence measure
Using Contextual Information To Selectively Adjust Preprocessing Parameters
Object segmentation using an array of interconnected neural networks with local receptive fields
Interactive Parts Model: an Application to Recognition of On-line Cursive Script
REFEREED PUBLICATIONS P. Neskovic, L. Wu and L. N. Cooper. Learning by Integrating Information Within and Across
SELECTING DATA FOR FAST SUPPORT VECTOR MACHINE TRAINING