#### Filter Results:

- Full text PDF available (52)

#### Publication Year

2001

2018

- This year (6)
- Last 5 years (23)
- Last 10 years (60)

#### Publication Type

#### Co-author

#### Journals and Conferences

#### Data Set Used

Learn More

- Mikhail Belkin, Partha Niyogi, Vikas Sindhwani
- Journal of Machine Learning Research
- 2006

We propose a family of learning algorithms based on a new form of regularization that allows us to exploit the geometry of the marginal distribution. We focus on a semi-supervised framework thatâ€¦ (More)

- Vikas Sindhwani, Partha Niyogi, Mikhail Belkin
- ICML
- 2005

Due to its occurrence in engineering domains and implications for natural learning, the problem of utilizing unlabeled data is attracting increasing attention in machine learning. A large body ofâ€¦ (More)

We propose a family of learning algorithms based on a new form of regularization that allows us to exploit the geometry of the marginal distribution. We focus on a semi-supervised framework thatâ€¦ (More)

- Misha Belkin, Partha Niyogi, Vikas Sindhwani
- AISTATS
- 2005

We propose a family of learning algorithms based on a new form of regularization that allows us to exploit the geometry of the marginal distribution. We focus on a semisupervised framework thatâ€¦ (More)

- Vikas Sindhwani, S. Sathiya Keerthi
- SIGIR
- 2006

Large scale learning is often realistic only in a semi-supervised setting where a small set of labeled examples is available together with a large collection of unlabeled data. In many informationâ€¦ (More)

- Olivier Chapelle, Vikas Sindhwani, S. Sathiya Keerthi
- Journal of Machine Learning Research
- 2008

Due to its wide applicability, the problem of semi-supervised classification is attracting increasing attention in machine learning. Semi-Supervised Support Vector Machines (S3VMs) are based onâ€¦ (More)

- Amol Ghoting, Rajasekar Krishnamurthy, +5 authors Shivakumar Vaithyanathan
- 2011 IEEE 27th International Conference on Dataâ€¦
- 2011

MapReduce is emerging as a generic parallel programming paradigm for large clusters of machines. This trend combined with the growing need to run machine learning (ML) algorithms on massive datasetsâ€¦ (More)

The Co-Training algorithm uses unlabeled examples in multiple views to bootstrap classifiers in each view, typically in a greedy manner, and operating under assumptions of view-independence andâ€¦ (More)

- Vikas Sindhwani, David S. Rosenberg
- ICML
- 2008

Inspired by co-training, many multi-view semi-supervised kernel methods implement the following idea: find a function in each of multiple Reproducing Kernel Hilbert Spaces (RKHSs) such that (a) theâ€¦ (More)

- Abhishek Kumar, Vikas Sindhwani, Prabhanjan Kambadur
- ICML
- 2013

The separability assumption (Donoho & Stodden, 2003; Arora et al., 2012a) turns non-negative matrix factorization (NMF) into a tractable problem. Recently, a new class of provably-correct NMFâ€¦ (More)