High accuracy learning by boosting weak learners

摘要

A system, apparatus, method, and computer-readable medium for optimizing classifiers are disclosed. The optimization process can include receiving one or more training examples. The optimization process can further include assigning a loss parameter to each training example. The optimization process can further include optimizing each loss parameter of each training sample based on a sample variance of each training example using a non-linear function. The optimization process can further include estimating a classifier from the one or more weighted training samples. The optimization process can further include assigning a loss parameter to the classifier based on a number of training examples that the classifier correctly classified and a number of training examples that the classifier incorrectly classified. The optimization process can further include adding the weighted classifier to an overall classifier.

主权项

1. A computer-implemented method, comprising:
receiving by a processor, training examples representing classifiable events or objects including label data identifying respective classes of the events or objects, wherein each training example comprises one or more elements; associating a respective loss parameter with each of the training examples, a value of each respective loss parameter being initialized to an initial value; (a) calculating a weight of each training example based on a sample variance of the training example using a non-linear function; (b) optimizing a weak learner in a pool of weak learners or selecting a classifier from a pool of classifiers, to minimize an exponential loss of the weak learner or classifier on the weighted training examples, without evaluating all the weak learners in the pool of weak learners and without evaluating all of the classifiers in the pool of classifiers; (c) calculating a coefficient for the optimized weak learner or the selected classifier which is proportional to a logarithm of the ratio of the sum of the assigned weights corresponding to the examples classified correctly by the optimized weak learner or the selected classifier and the sum of the assigned weights corresponding to the examples incorrectly classified by the optimized weak learner or the selected classifier; (d) updating the loss parameters to the product of each with the exponential loss of the weak learner or classifier on its respective training example; repeating the operations defined in clauses a through d until a stop criterion is met; forming a linear combination of the optimized weak learners or the selected classifiers obtained from multiple iterations of operations a through d, each weighted by a respective one of the coefficients calculated in operation c; and outputting data representing said linear combination.