Feature selection for automatic classification of non-Gaussian data
IEEE Transactions on Systems, Man and Cybernetics - Special issue on artificial intelligence
Editing for the k-nearest neighbors rule by a genetic algorithm
Pattern Recognition Letters - Special issue on genetic algorithms
Pattern Recognition Letters
Feature Selection: Evaluation, Application, and Small Sample Performance
IEEE Transactions on Pattern Analysis and Machine Intelligence
How Easy is Matching 2D Line Models Using Local Search?
IEEE Transactions on Pattern Analysis and Machine Intelligence
Prototype selection for the nearest neighbour rule through proximity graphs
Pattern Recognition Letters
A Discrete Expression of Canny's Criteria for Step Edge Detector Performances Evaluation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Selection of the optimal prototype subset for 1-NN classification
Pattern Recognition Letters
Goal-Directed Evaluation of Binarization Methods
IEEE Transactions on Pattern Analysis and Machine Intelligence
Numerical Methods for Unconstrained Optimization and Nonlinear Equations (Classics in Applied Mathematics, 16)
Estimation of Dependences Based on Empirical Data: Springer Series in Statistics (Springer Series in Statistics)
The application of multiwavelet filterbanks to image processing
IEEE Transactions on Image Processing
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A technique is proposed for choosing the thresholds for a number of object detection tasks, based on a prototype selection technique. The chosen prototype subset has to be correctly classified. The positive and negative objects are introduced in order to provide the optimization via empirical risk minimization. A Boolean function and its derivatives are obtained for each object. A special technique, based on the fastest gradient descent, is proposed for the sum of Boolean functions maximization. The method is applied to the detection task of house edges, using its images in aerial photos. It is shown that proposed method can be expanded to solving of a wide range of tasks, connected to the function optimization, while the function is given in vertices of a 2n single hyper - cube.