In this paper, we study an online learning algorithm in Reproducing Kernel Hilbert Spaces (RKHS) and general Hilbert spaces. We present a general form of the stochastic gradient m...
We apply Stochastic Meta-Descent (SMD), a stochastic gradient optimization method with gain vector adaptation, to the training of Conditional Random Fields (CRFs). On several larg...
S. V. N. Vishwanathan, Nicol N. Schraudolph, Mark ...
The paper illustrates a linguistic knowledge acquisition model making use of data types, innite memory, and an inferential mechanism for inducing new information from known data. ...
Paolo Allegrini, Simonetta Montemagni, Vito Pirrel...
In this work we study some probabilistic models for the random generation of words over a given alphabet used in the literature in connection with pattern statistics. Our goal is t...
For birth and death processes with finite state space consisting of N + 1 points (N 2), we consider stochastic processes induced by conditioning on hitting the right boundary poi...