Learning Model Complexity in an Online Environment

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 ex...

• Type: Conference
• Year: 2009
• Where: CRV
• Authors: Dan Levi, Shimon Ullman
• Keywords: CRV 2009, Model, Model Complexity, Robotics, Training Examples