Modern machine learning tasks often deal with high-dimensional data. One typically makes some assumption on structure, like sparsity, to make learning tractable over high-dimensional instances. Another common assumption on structure is that of latent variables in the generative model. In latent variable models, one attempts to perform inference not only on observed variables, but also on unobse...

Syntactic reordering on the source-side is an effective way of handling word order differences. The { (DE) construction is a flexible and ubiquitous syntactic structure in Chinese which is a major source of error in translation quality. In this paper, we propose a new classifier model — discriminative latent variable model (DPLVM) — to classify the DE construction to improve the accuracy of the...

We use a generative history-based model to predict the most likely derivation of a dependency parse. Our probabilistic model is based on Incremental Sigmoid Belief Networks, a recently proposed class of latent variable models for structure prediction. Their ability to automatically induce features results in multilingual parsing which is robust enough to achieve accuracy well above the average ...

May 11, 2010 Title Latent Variable Analysis Version 0.3-1 Author Yves Rosseel Maintainer Yves Rosseel Description Fit a variety of latent variable models, including confirmatory factor analysis, structural equation modeling and latent growth curve models. Depends R (>= 2.10.1), methods License GPL-2 LazyLoad yes LazyData yes URL http://lavaan.org ...

In this paper, we develop a weakly supervised version of logistic regression to help to improve biomedical text classification performance when there is limited annotated data. We learn cascaded latent variable models for the classification tasks. First, with a large number of unlabelled but limited amount of labelled biomedical text, we will bootstrap and semi-automate the annotation task with...

This paper presents a latent variable structured prediction model for discriminative supervised clustering of items called the Latent Left-linking Model (LM). We present an online clustering algorithm for LM based on a feature-based item similarity function. We provide a learning framework for estimating the similarity function and present a fast stochastic gradient-based learning technique. In...