We provide a worst-case analysis of selective sampling algorithms for learning linear threshold functions. The algorithms considered in this paper are Perceptron-like algorithms, ...
Probabilistic mixture models are used for a broad range of data analysis tasks such as clustering, classification, predictive modeling, etc. Due to their inherent probabilistic na...
A key question in neuroscience is how to encode sensory stimuli such as images and sounds. Motivated by studies of response properties of neurons in the early cortical areas, we p...
Javid Sadr, Sayan Mukherjee, K. Thoresz, Pawan Sin...
Bayesian network models are widely used for discriminative prediction tasks such as classification. Usually their parameters are determined using 'unsupervised' methods ...
Many techniques have been reported for handwriting-based writer identification. Most such techniques assume that the written text is fixed (e.g., in signature verification). In th...
H. E. S. Said, G. S. Peake, T. N. Tan, Keith D. Ba...