Prior knowledge, in the form of simple advice rules, can greatly speed up convergence in learning algorithms. Online learning methods predict the label of the current point and the...
Gautam Kunapuli, Kristin P. Bennett, Amina Shabbee...
In this paper we study a paradigm to generalize online classification algorithms for binary classification problems to multiclass problems. The particular hypotheses we investig...
In this paper, a new algorithm for robust adaptive beamforming is developed. The basic idea of the proposed algorithm is to estimate the difference between the actual and presumed...
Aboulnasr Hassanien, Sergiy A. Vorobyov, Kon Max W...
This paper proposes, for the FPGA-based embedded systems, a reliability-aware process scheduling strategy that operates under performance bounds. A unique characteristic of the pr...
Guilin Chen, Mahmut T. Kandemir, Suleyman Tosun, U...
The kernel Perceptron is an appealing online learning algorithm that has a drawback: whenever it makes an error it must increase its support set, which slows training and testing ...