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, ...
Making vital disk data recoverable even in the event of OS compromises has become a necessity, in view of the increased prevalence of OS vulnerability exploits over the recent yea...
We propose a well-founded method of ranking a pool of m trained classifiers by their suitability for the current input of n instances. It can be used when dynamically selecting a s...
Classifier fusion is considered as one of the best strategies for improving performances upon general purpose classification systems. On the other hand, fusion strategy space stro...
Wael Ben Soltana, Mohsen Ardabilian, Liming Chen, ...
Tuning SVM hyperparameters is an important step in achieving a high-performance learning machine. It is usually done by minimizing an estimate of generalization error based on the...