Supervised learning rule selection for multiclass decision with performance constraints

International audienceA procedure to select a supervised rule for multiclass problem from a labeled dataset is proposed. The rule allows class-selective rejection and performance constraints. The unknown probabilities are estimated with a Parzen estimator. A set of rules are built by varying the Parzen¿s smoothness parameter of the marginal probabilities estimates and plugging them into the statistical hypothesis rules. A criterion that assesses the quality of these rules is estimated and used to select a rule. Resampling and aggregation methods are used to show the efficiency of the estimated criterion

Pattern Recognition

Pattern recognition is a very wide research field. It involves factors as diverse as sensors, feature extraction, pattern classification, decision fusion, applications and others. The signals processed are commonly one, two or three dimensional, the processing is done in real- time or takes hours and days, some systems look for one narrow object class, others search huge databases for entries with at least a small amount of similarity. No single person can claim expertise across the whole field, which develops rapidly, updates its paradigms and comprehends several philosophical approaches. This book reflects this diversity by presenting a selection of recent developments within the area of pattern recognition and related fields. It covers theoretical advances in classification and feature extraction as well as application-oriented works. Authors of these 25 works present and advocate recent achievements of their research related to the field of pattern recognition