On a Robust MaxEnt Process Regression Model with Sample-Selection

Estimation of a regression spline sample selection model

It is often the case that an outcome of interest is observed for a restricted non-randomly selected sample of the population. In such a situation, standard statistical analysis yields biased results. This issue can be addressed using sample selection models which are based on the estimation of two regressions: a binary selection equation determining whether a particular statistical unitwill be ...

Transductive Gaussian Process Regression with Automatic Model Selection

In contrast to the standard inductive inference setting of predictive machine learning, in real world learning problems often the test instances are already available at training time. Transductive inference tries to improve the predictive accuracy of learning algorithms by making use of the information contained in these test instances. Although this description of transductive inference appli...

Small-Sample Model-Robust Confidence Intervals in Regression

Period Disambiguation with Maxent Model

This paper presents our recent work on period disambiguation, the kernel problem in sentence boundary identification, with the maximum entropy (Maxent) model. A number of experiments are conducted on PTB-II WSJ corpus for the investigation of how context window, feature space and lexical information such as abbreviated and sentence-initial words affect the learning performance. Such lexical inf...

Abbreviation Recognition with MaxEnt Model

Abbreviated words carry critical information in the literature of many special domains. This paper reports our research in recognizing dotted abbreviations with MaxEnt model. The key points in our work include: (1) allowing the model to optimize with as many features as possible to capture the text characteristics of context words, and (2) utilizing simple lexical information such as sentence-i...