This page deals with the problem of how to incrementally build a generative model from data using a positive as well as negative examples. We propose an incremental update rule for building one dimensional Gaussian mixture models based on Kernel Density Estimation that support learning from negative examples.

This page deals with the problem of how to design an algorithm that would use as little assumptions as possible about the input data and would allow building a generative model by observing only a single or a few data points at a time. The algorithm is based on multivariate Kernel Density Estimator which uses a revitalization scheme and is robust to data ordering.

We present an algorithm for building a discriminative models by observing only a single or a few data points at a time. The algorithm is and extension of the multivariate online Kernel Density Estimator, and uses a new measure of discrimination loss to determine how much a classifier can be compressed without modyfing its performance.