Bias/variance analysis is a useful tool for investigating the performance of machine learning algorithms. Conventional analysis decomposes loss into errors due to aspects of the le...
The perceptron (also referred to as McCulloch-Pitts neuron, or linear threshold gate) is commonly used as a simplified model for the discrimination and learning capability of a bi...
Abstract. This paper introduces the model-based software development lifecycle model SMA--the Smyle Modeling Approach--which is centered around Smyle. Smyle is a dedicated learning...
We investigate the use of linear and nonlinear principal manifolds for learning low-dimensional representations for visual recognition. Three techniques: Principal Component Analy...
We prove the strongest known bound for the risk of hypotheses selected from the ensemble generated by running a learning algorithm incrementally on the training data. Our result i...