This paper describes conditional-probability training of Markov random fields using combinations of labeled and unlabeled data. We capture the similarities between instances learning the appropriate distance metric from the data. The likelihood model and several training procedures are presented.

Recently, approaches have been put forward that focus on the recognition of mesh semantic meanings. These methods usually need prior knowledge learned from training dataset, but when the size of the training dataset is small, or the meshes are too complex, the segmentation performance will be greatly effected. This paper introduces an approach to the semantic mesh segmentation and labeling whic...

A δ-uniform BSS machine is a standard BSS machine which does not rely on exact equality tests. We prove that, for any real closed archimedean field R, a set is δ-uniformly semi-decidable iff it is open and semi-decidable by a BSS machine which is locally time bounded; we also prove that the local time bound condition is nontrivial. This entails a number of results about BSS machines, in particu...

We experiment graph-based SemiSupervised Learning (SSL) of Conditional Random Fields (CRF) for the application of Spoken Language Understanding (SLU) on unaligned data. The aligned labels for examples are obtained using IBM Model. We adapt a baseline semisupervised CRF by defining new feature set and altering the label propagation algorithm. Our results demonstrate that our proposed approach si...

This paper extends and generalizes the Bayesian semisupervised segmentation algorithm [1] for oil spill detection using SAR images. In the base algorithm on which we build on, the data term is modeled by a finite mixture of Gamma distributions. The prior is an Mlevel logistic Markov Random Field enforcing local continuity in a statistical sense. The methodology proposed in [1] assumes two class...

Electroand magnetorheological fluids are smart, synthetic fluids changing their viscosity from liquid to semisolid state within milliseconds if a sufficiently strong electric or magnetic field is applied. When used in suitable devices, they offer the innovative potential of very fast, adaptively controllable interfaces between mechanical devices and electronic control units. This paper gives an...

In this paper, we propose a semi-supervised learning method to simultaneous segmentation and labeling of parts in 3D garments. The key idea in this work is to analyze 3D garments using semi-supervised learning method which can label parts in various 3D garments. We first develop an objective function based on Conditional Random Field (CRF) model to learn the prior knowledge of garment component...

This paper presents a novel hybrid generative/discriminative model of word segmentation based on nonparametric Bayesian methods. Unlike ordinary discriminative word segmentation which relies only on labeled data, our semi-supervised model also leverages a huge amounts of unlabeled text to automatically learn new “words”, and further constrains them by using a labeled data to segment non-standar...

We present novel methods to construct compact natural language lexicons within a graphbased semi-supervised learning framework, an attractive platform suited for propagating soft labels onto new natural language types from seed data. To achieve compactness, we induce sparse measures at graph vertices by incorporating sparsity-inducing penalties in Gaussian and entropic pairwise Markov networks ...