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- Christopher J. C. Burges
- Data Mining and Knowledge Discovery
- 1998

The tutorial starts with an overview of the concepts of VC dimension and structural risk minimization. We then describe linear Support Vector Machines (SVMs) for separable and non-separable data,â€¦ (More)

Training a support vector machine (SVM) leads to a quadratic optimization problem with bound constraints and one linear equality constraint. Despite the fact that this type of problem is wellâ€¦ (More)

- Christopher J. C. Burges, Tal Shaked, +4 authors Gregory N. Hullender
- ICML
- 2005

We investigate using gradient descent methods for learning ranking functions; we propose a simple probabilistic cost function, and we introduce RankNet, an implementation of these ideas using aâ€¦ (More)

A new regression technique based on Vapnikâ€™s concept of support vectors is introduced. We compare support vector regression (SVR) with a committee regression technique (bagging) based on regressionâ€¦ (More)

- Bernhard SchÃ¶lkopf, Sebastian Mika, +4 authors Alexander J. Smola
- IEEE Trans. Neural Networks
- 1999

This paper collects some ideas targeted at advancing our understanding of the feature spaces associated with support vector (SV) kernel functions. We first discuss the geometry of feature space. Inâ€¦ (More)

- Christopher J. C. Burges, Robert Ragno, Quoc V. Le
- NIPS
- 2006

The quality measures used in information retrieval are particularly difficult to optimize directly, since they depend on the model scores only through the sorted order of the documents returned for aâ€¦ (More)

LambdaMART is the boosted tree version of LambdaRank, which is based on RankNet. RankNet, LambdaRank, and LambdaMART have proven to be very successful algorithms for solving real world rankingâ€¦ (More)

- Qiang Zheng, Christopher J. C. Burges, Krysta Marie Svore, Jianfeng Gao
- Information Retrieval
- 2009

We present a new ranking algorithm that combines the strengths of two previous methods: boosted tree classification, and LambdaRank, which has been shown to be empirically optimal for a widely usedâ€¦ (More)

- Christopher J. C. Burges
- ICML
- 1996

A Support Vector Machine (SVM) is a universal learning machine whose decision surface is parameterized by a set of support vectors , and by a set of corresponding weights. An SVM is alsoâ€¦ (More)

We present MCTest, a freely available set of stories and associated questions intended for research on the machine comprehension of text. Previous work on machine comprehension (e.g., semanticâ€¦ (More)