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- Corinna Cortes, Vladimir Vapnik
- Machine Learning
- 1995

Thesupport-vector network is a new learning machine for two-group classification problems. The machine conceptually implements the following idea: input vectors are non-linearly mapped to a veryâ€¦ (More)

- Corinna Cortes, Mehryar Mohri
- NIPS
- 2003

The area under an ROC curve (AUC) is a criterion used in many applications to measure the quality of a classification algorithm. However, the objective function optimized in most of these algorithmsâ€¦ (More)

- Corinna Cortes, Mehryar Mohri, Afshin Rostamizadeh
- NIPS
- 2009

This paper studies the general problem of learning kernels based on a polynomial combination of base kernels. We analyze this problem in the case of regression and the kernel ridge regressionâ€¦ (More)

- Corinna Cortes, Mehryar Mohri, Afshin Rostamizadeh
- ICML
- 2010

This paper examines two-stage techniques for learning kernels based on a notion of alignment. It presents a number of novel theoretical, algorithmic, and empirical results for alignmentbasedâ€¦ (More)

- Corinna Cortes, Mehryar Mohri, Afshin Rostamizadeh
- Journal of Machine Learning Research
- 2012

This paper presents new and effective algorithms for learning kernels. In particular, as shown by our empirical results, these algorithms consistently outperform the so-called uniform combinationâ€¦ (More)

- Corinna Cortes, Mehryar Mohri, Afshin Rostamizadeh
- ICML
- 2010

This paper presents several novel generalization bounds for the problem of learning kernels based on a combinatorial analysis of the Rademacher complexity of the corresponding hypothesis sets. Ourâ€¦ (More)

The choice of the kernel is critical to the success of many learning algorithms but it is typically left to the user. Instead, the training data can be used to learn the kernel by selecting it out ofâ€¦ (More)

- Corinna Cortes, Mehryar Mohri, Jason Weston
- ICML
- 2005

The problem of learning a transduction, that is a string-to-string mapping, is a common problem arising in natural language processing and computational biology. Previous methods proposed forâ€¦ (More)

This paper presents a theoretical analysis of sample selection bias correction. The sample bias correction technique commonly used in machine learning consists of reweighting the cost of an error onâ€¦ (More)

This paper compares the performance of several classi er algorithms on a standard database of handwritten digits. We consider not only raw accuracy, but also training time, recognition time, andâ€¦ (More)