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- Timothy L. Bailey, Charles Elkan
- ISMB
- 1994

The algorithm described in this paper discovers one or more motifs in a collection of DNA or protein sequences by using the technique of expectation maximization to fit a two-component finite mixtureâ€¦ (More)

- Charles Elkan
- IJCAI
- 2001

This paper revisits the problem of optimal learning and decision-making when different misclassification errors incur different penalties. We characterize precisely but intuitively when a cost matrixâ€¦ (More)

- Charles Elkan, Keith Noto
- KDD
- 2008

The input to an algorithm that learns a binary classifier normally consists of two sets of examples, where one set consists of positive examples of the concept to be learned, and the other setâ€¦ (More)

- Timothy L. Bailey, Charles Elkan
- Machine Learning
- 1995

The MEME algorithm extends the expectation maximization (EM) algorithm for identifying motifs in unaligned biopolymer sequences. The aim of MEME is to discover new motifs in a set of biopolymerâ€¦ (More)

- Timothy L. Bailey, Charles Elkan
- ISMB
- 1995

MEME is a tool for discovering motifs in sets of protein or DNA sequences. This paper describes several extensions to MEME which increase its ability to find motifs in a totally unsupervised fashion,â€¦ (More)

- Charles Elkan
- ICML
- 2003

The -means algorithm is by far the most widely used method for discovering clusters in data. We show how to accelerate it dramatically, while still always computing exactly the same result as theâ€¦ (More)

- Greg Hamerly, Charles Elkan
- NIPS
- 2003

When clustering a dataset, the right number k of clusters to use is often not obvious, and choosing k automatically is a hard algorithmic problem. In this paper we present a new algorithm forâ€¦ (More)

- Bianca Zadrozny, Charles Elkan
- KDD
- 2002

Class membership probability estimates are important for many applications of data mining in which classification outputs are combined with other sources of information for decision-making, such asâ€¦ (More)

- Alvaro E. Monge, Charles Elkan
- KDD
- 1996

To combine information from heterogeneous sources, equivalent data in the multiple sources must be identified. This task is the field matching problem. Specifically, the task is to determine whetherâ€¦ (More)

- Bianca Zadrozny, Charles Elkan
- ICML
- 2001

Accurate, well-calibrated estimates of class membershipprobabilities are neededin many supervisedlearning applications, in particular when a cost-sensiti ve decision must be made aboutexampleswithâ€¦ (More)