John D. Lafferty, Andrew McCallum, Fernando C. N. Pereira
We present conditional random fields, a framework for building probabilistic models to segment and label sequence data. Conditional random fields offer several advantages over hidden Markov models and stochastic grammars for such tasks, ...
Kiri Wagstaff, Claire Cardie, Seth Rogers, Stefan SchrÃ¶dl
Clustering is traditionally viewed as an unsupervised method for data analysis. However, in some cases information about the problem domain is available in addition to the data instances themselves. In this paper, we demonstrate how the popular ...
This paper presents an active learning method that directly optimizes expected future error. This is in contrast to many other popular techniques that instead aim to reduce version space size. These methods are popular because for many learning ...
Many application domains suffer from not having enough labeled training data for learning. However, large...
Bianca Zadrozny, Charles Elkan
Accurate, well-calibrated estimates of class membership probabilities are needed in many supervised learning applications, in particular when a cost-sensitive decision must be made about examples with example-dependent costs. This paper presents ...
Eric P. Xing, Michael I. Jordan, Richard M. Karp
We report on the successful application of feature selection methods to a classification problem in molecular biology involving only 72 data points in a 7130 dimensional space. Our approach is a hybrid of filter and wrapper approaches to feature ...
Thorsten Joachims, Nello Cristianini, John Shawe-Taylor
We propose to scale learning algorithms to arbitrarily large databases by the following method. First derive an upper bound for the learner's loss as a function of the number of examples used in each step of the algorithm. Then use this to ...
This paper introduces multiple instance regression, a variant of multiple regression in which each data point may be described by more than one vector of values for the independent variables. The goals of this work are to (1) understand the ...
Recommender systems use ratings from users on items such as movies and music for the purpose of predicting the user preferences on items that have not been rated. Predictions are normally done by using the ratings of other users of the system, by ...