This paper develops theoretical results regarding noisy 1-bit compressed sensing and sparse binomial regression. We demonstrate that a single convex program gives an accurate estim...
Applications that adapt to a particular end user often make inaccurate predictions during the early stages when training data is limited. Although an end user can improve the lear...
Weng-Keen Wong, Ian Oberst, Shubhomoy Das, Travis ...
In the standard formalization of supervised learning problems, a datum is represented as a vector of features without prior knowledge about relationships among features. However, ...
Density ratio estimation has gathered a great deal of attention recently since it can be used for various data processing tasks. In this paper, we consider three methods of densit...
Which active learning methods can we expect to yield good performance in learning binary and multi-category logistic regression classifiers? Addressing this question is a natural ...
This paper describes a new method for a feature-based supervised classification of multi-channel SAR data. Classic feature selection and classification methods are inadequate due ...
This paper proposes a theoretical framework for predicting financial distress based on Hunt’s (2000) Resource-Advantage Theory of Competition. The study focuses on the US retail...
High-throughput methods can directly detect the set of interacting proteins in yeast but the results are often incomplete and exhibit high false positive and false negative rates....
Because most of the procedures in defect inspection process of TFT-LCD module assembly are examined manually through human vision, cycle time estimation for this particular process...
In this paper we will briefly describe the approaches taken by the Cheshire (Berkeley) Group for the CLEF Adhoc-TEL 2009 tasks (Mono and Bilingual retrieval). Recognizing that man...