We address the learning of trust based on past observations and context information. We argue that from the truster's point of view trust is best expressed as one of several ...
We introduce a novel framework for simultaneous structure and parameter learning in hidden-variable conditional probability models, based on an entropic prior and a solution for i...
Hidden Markov Models (HMMs) are the most commonly used acoustic model for speech recognition. In HMMs, the probability of successive observations is assumed independent given the ...
In part of speech tagging by Hidden Markov Model, a statistical model is used to assign grammatical categories to words in a text. Early work in the field relied on a corpus which...
The performance of multi-hop CSMA/CA networks has in most cases been evaluated via simulations, or analytically using a perfect collision channel model. Using such methods, one ca...