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Building Grounded Abstractions for Artificial Intelligence Programming
(2004-06-16)
Most Artificial Intelligence (AI) work can be characterized as either ``high-level'' (e.g., logical, symbolic) or ``low-level'' (e.g., connectionist networks, behavior-based robotics). Each approach suffers from particular ...
A Note on the Generalization Performance of Kernel Classifiers with Margin
(2000-05-01)
We present distribution independent bounds on the generalization misclassification performance of a family of kernel classifiers with margin. Support Vector Machine classifiers (SVM) stem out of this class of machines. The ...
Building Grounded Abstractions for Artificial Intelligence Programming
(2004-06-16)
Most Artificial Intelligence (AI) work can be characterized as either ``high-level'' (e.g., logical, symbolic) or ``low-level'' (e.g., connectionist networks, behavior-based robotics). Each approach suffers from particular ...
A Trainable System for Object Detection in Images and Video Sequences
(2000-05-01)
This thesis presents a general, trainable system for object detection in static images and video sequences. The core system finds a certain class of objects in static images of completely unconstrained, cluttered scenes ...