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We present a novel and flexible approach to the problem of feature selection, called grafting. Rather than considering feature selection as separate from learning, grafting treats the selection of suitable features as an integral part of learning a predictor in a regularized learning framework. To make this regularized learning process sufficiently fast for(More)
  • Kim Binsted, David Asher, Jim Broughton, Don Casadonte, William Chesters, Myles Chippendale +15 others
  • 1996
1996 \Judging from their laughter, the children at school found my remarks humorous. So without understanding humor, I have somehow mastered it." { L a l , i n S t a r T rek, \The Ospring" Abstract This thesis describes a formal model of a subtype of humour, and the implementation of that model in a program that generates jokes of that subtype. Although(More)
We introduce an algorithm for classifying time series data. Since our initial application is for lightning data, we call the algorithm Zeus. Zeus is a hybrid algorithm that employs evolutionary computation for feature extraction, and a support vector machine for the final " backend " classification. Support vector machines have a reputation for classifying(More)
Batch implementations of support vector regression (SVR) are inefficient when used in an on-line setting because they must be retrained from scratch every time the training set is modified. Following an incremental support vector classification algorithm introduced by Cauwenberghs and Poggio (2001), we have developed an accurate on-line support vector(More)
Figure 1: Selective Refinement. The headrest is an object of African cultural heritage. In each frame the selected region is refined progressively. Abstract We present a framework for real-time view-dependent refinement, and adapt it to the task of browsing large model repositories on the Internet. We introduce a novel hierarchical representation of atomic(More)
We consider the problem of pixel-by-pixel classification of a multi-spectral image using supervised learning. Conventional supervised classification techniques such as maximiun likelihood classifcation and less conventional ones such as neural networks, typically base such classifications solely on the spectral components of each pixel. It is easy to see(More)
Multi-instrument data sets present an interesting challenge to feature extraction algorithm developers. Beyond the immediate problems of spatial co-registration, the remote sensing scientist must explore a complex algorithm space in which both spatial and spectral signatures may be required to identify a feature of interest. We describe a genetic(More)