Ajay Mathur

Artificial Intelligence · 2004–2008

A researcher in the Science corpus: 5 papers; trusts 8 researchers and is trusted by 34 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 19 citations into it; #24,517 of 35,345 by trust here, #29,412 by citations
• Media Technology — 17 citations into it; #532 of 3,621 by trust here, #660 by citations
• Ecology — 11 citations into it; #429 of 10,717 by trust here, #2,340 by citations
• Computer Vision and Pattern Recognition — 9 citations into it; #6,360 of 21,575 by trust here, #9,563 by citations
• Atmospheric Science — 7 citations into it
• Control and Systems Engineering — 5 citations into it
• Scaling & Learning Theory — 5 citations into it

Whom Ajay Mathur trusts overall

Their strongest outbound trust, strongest first, inferred from their citations.

1. Giles M. Foody
2. Vladimir Naumovich Vapni
3. Yakoub Bazi
4. Chih-Wei Hsu
5. Farid Melgani
6. Chih-Jen Lin
7. Mahesh Pal
8. Paul M. Mather

Who trusts Ajay Mathur overall

1. Giles M. Foody
2. Björn Waske
3. Isaac Kwesi Nooni
4. Elhadi Adam
5. Jon Atli Benediktsson
6. Annemarie Schneider
7. Swapan Talukdar
8. Yakoub Bazi
9. Onisimo Mutanga
10. Xin Huang

Papers from Ajay Mathur's perspective

Ordered by how strongly the researchers Ajay Mathur trusts endorse each paper. Same corpus, a different order for every researcher.

1. The Nature of Statistical Learning Theory
2. Toward an Optimal SVM Classification System for Hyperspectral Remote Sensing Images (IEEE Transactions on Geoscience and Remote Sensing)
3. An introduction to kernel-based learning algorithms
4. Support vector machines for hyperspectral image classification with spectral-based kernels (IGARSS 2003. 2003 IEEE International Geoscience and Remote Sensing Symposium. Proceedings (IEEE Cat. No.03CH37477))
5. An assessment of the effectiveness of decision tree methods for land cover classification
6. Training set size requirements for the classification of a specific class
7. Computer processing of remotely sensed images
8. Support-Vector Networks
9. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
10. A training algorithm for optimal margin classifiers

Their papers

• Multiclass and Binary SVM Classification: Implications for Training and Classification Users (2008) — 369 citations
• Training set size requirements for the classification of a specific class (2006) — 320 citations
• The use of small training sets containing mixed pixels for accurate hard image classification: Training on mixed spectral responses for classification by a SVM (2006) — 443 citations
• A relative evaluation of multiclass image classification by support vector machines (2004) — 1,088 citations
• Toward intelligent training of supervised image classifications: directing training data acquisition for SVM classification (2004) — 647 citations