Learning Model Complexity in an Online Environment

Abstract

In this paper we introduce the concept and method for adaptively tuning the model complexity in an online manner as more examples become available. Challenging classification problems in the visual domain (such as recognizing handwriting, faces and human-body images) often require a large number of training examples, which may become available over a long training period. This motivates the development of scalable and adaptive systems which are able to continue learning at any stage and which can efficiently learn from large amounts of data, in an on-line manner. Previous approaches to on-line learning in visual classification have used a fixed parametric model, and focused on continuously improving the model parameters as more data becomes available. Here we propose a new framework which enables online learning algorithms to adjust the complexity of the learned model to the amount of the training data as more examples become available. Since in online learning the training set expands over time, it is natural to allow the learned model to become more complex during the course of learning instead of confining the model to a fixed family of a bounded complexity. Formally, we use a set of parametric classifiers y = hαθ (x) where y is the class and x the observed data. The parameter α controls the complexity of the model family. For a fixed α, the training examples are used for the optimal setting of θ. When the amount of data becomes sufficiently large, the value of α is increased, and a more complex model family is used. For evaluation of the proposed approach, we implement an online Support Vector Machine with increasing complexity, and evaluate in a task of handwritten character recognition on the MNIST database.