The Co-Training algorithm uses unlabeled examples in multiple views to bootstrap classifiers in each view, typically in a greedy manner, and operating under assumptions of view-independence and compatibility. In this paper, we propose a Co-Regularization framework where classifiers are learnt in each view through forms of multi-view regularization. We propose algorithms within this framework th...

teacher autonomy is viewed very differently: one teacher may view autonomy as a means to gain substantial freedom from interference or supervision; another may view it as the freedom to develop collegial relationships and accomplish tasks that extend beyond the classroom; and even some others may perceive it as a means for principals to avoid their duties (frase and sorenson, 1992). although th...

In this paper, we extend Smets’ transferable belief model (TBM) with probabilistic priors. Our first motivation for the extension is about evidential reasoning when the underlying prior knowledge base is Bayesian. We extend standard Dempster models with prior probabilities to represent beliefs and distinguish between two types of induced mass functions on an extended Dempster model: one for bel...

Clostridium botulinum neurotoxins (BoNTs) are effective therapeutics for a variety of neurological disorders, such as strabismus, blepharospam, hemificial spasm, and cervical dystonia, because of the toxin's tropism for neurons and specific cleavage of neuronal soluble N-ethylmaleimide-sensitive fusion protein-attachment protein receptors (SNARE) proteins. Modifying BoNT to bind nonneuronal cel...

We present two methods to address the problem of sparsity in the FrameNet lexical database. The first method is based on the idea that a word that belongs to a frame is “similar” to the other words in that frame. We measure the similarity using a WordNetbased variant of the Lesk metric. The second method uses the sequence of synsets in WordNet hypernym trees as feature vectors that can be used ...