G. Dietterich

Journal of Machine Learning Research 4 (2003) 933-969 Submitted 12/01; Revised 11/02; Published 11/03 An Efficient Boosting Algorithm for Combining Preferences (2004)

Yoav Freund, Raj Iyer, Robert E. Schapire, Yoram Singer, G. Dietterich

We study the problem of learning to accurately rank a set of objects by combining a given collection of ranking or preference functions. This problem of combining preferences arises in several...

Journal of Machine Learning Research 5 (2004) 361-397 Submitted 9/02; Published 4/04 RCV1: A New Benchmark Collection (2004)

David D. Lewis, Yiming Yang, Tony G. Rose, Fan Li, G. Dietterich

Reuters Corpus Volume I (RCV1) is an archive of over 800,000 manually categorized newswire stories recently made available by Reuters, Ltd. for research purposes. Use of this data for research on...

Journal of Machine Learning Research 4 (2003) 933-969 Submitted 12/01; Revised 11/02; Published 11/03 An Efficient Boosting Algorithm for Combining Preferences (2003)

Yoav Freund, Raj Iyer, Robert E. Schapire, Yoram Singer, G. Dietterich

We study the problem of learning to accurately rank a set of objects by combining a given collection of ranking or preference functions. This problem of combining preferences arises in several...

Journal of Machine Learning Research 4 (2003) 649--682 Submitted 12/01; Revised 11/02; Published 9/03 Speedup Learning for Repair-based Search (2003)

Shaul Markovitch, Asaf Shatil, G. Dietterich

Repair-based search algorithms start with an initial solution and attempt to improve it by iteratively applying repair operators. Such algorithms can often handle large-scale problems that may be...

Journal of Machine Learning Research 4 (2003) 39-66 Submitted 3/02; Revised 10/02; Published 4/03 Designing Committees of Models through Deliberate Weighting of (2003)

Stefan W. Christensen, Ian Sinclair, G. Dietterich

In the adaptive derivation of mathematical models from data, each data point should contribute with a weight reflecting the amount of confidence one has in it. When no additional information for data...

Evaluation and Selection of Biases in Machine Learning (1997)

G. Dietterich

In this introduction, we define the term bias as it is used in machine learning systems. We motivate the importance of automated methods for evaluating and selecting biases using a framework of bias...

Evaluation and Selection of Biases in Machine Learning (1995)

Diana F. Gordon, G. Dietterich

In this introduction, we define the term bias as it is used in machine learning systems. We motivate the importance of automated methods for evaluating and selecting biases using a framework of bias...

Evaluation and Selection of Biases in Machine Learning (1995)

Diana F. Gordon, G. Dietterich

. In this introduction, we define the term bias as it is used in machine learning systems. We motivate the importance of automated methods for evaluating and selecting biases using a framework of...