Abstract. In this paper, we propose a framework for learning the parameters of registration cost functions ? such as the tradeoff between the regularization and image similiarity t...
B. T. Thomas Yeo, Mert R. Sabuncu, Polina Gollan...
We present conditional random fields, a framework for building probabilistic models to segment and label sequence data. Conditional random fields offer several advantages over hid...
John D. Lafferty, Andrew McCallum, Fernando C. N. ...
This paper presents a wavelet neural-network for learning and approximation of chaotic time series. Wavelet-networks are inspired by both feed-forward neural networks and the theo...
Abstract. Hypertext categorization is the task of automatically assigning category labels to hypertext units. Comparable to text categorization it stays in the area of function lea...
Abstract. Conditional Random Fields (CRFs) provide a powerful instrument for labeling sequences. So far, however, CRFs have only been considered for labeling sequences over flat al...