Shankar Vembu
Shankar Vembu
Unknown affiliation
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Cited by
Cited by
Chemical gas sensor drift compensation using classifier ensembles
A Vergara, S Vembu, T Ayhan, MA Ryan, ML Homer, R Huerta
Sensors and Actuators B: Chemical 166, 320-329, 2012
PhyloWGS: reconstructing subclonal composition and evolution from whole-genome sequencing of tumors
AG Deshwar, S Vembu, CK Yung, GH Jang, L Stein, Q Morris
Genome biology 16, 1-20, 2015
Inferring clonal evolution of tumors from single nucleotide somatic mutations
W Jiao, S Vembu, AG Deshwar, L Stein, Q Morris
BMC bioinformatics 15, 1-16, 2014
DOLCE ergo SUMO: On foundational and domain models in the SmartWeb Integrated Ontology (SWIntO)
D Oberle, A Ankolekar, P Hitzler, P Cimiano, M Sintek, M Kiesel, ...
Journal of Web Semantics 5 (3), 156-174, 2007
Label ranking algorithms: A survey
S Vembu, T Gärtner
Preference learning, 45-64, 2010
Separation of vocals from polyphonic audio recordings
S Vembu, S Baumann
Proceedings of the 6th International Conference of Music Information Retrieval, 2005
Beam search algorithms for multilabel learning
A Kumar, S Vembu, AK Menon, C Elkan
Machine learning 92, 65-89, 2013
Predicting accurate probabilities with a ranking loss
A Menon, X Jiang, S Vembu, C Elkan, L Ohno-Machado
Proceedings of the 29th International Conference on Machine Learning, 2012
Towards bridging the semantic gap in multimedia annotation and retrieval
S Vembu, M Kiesel, M Sintek, S Baumann
Proceedings of the 1st International Workshop on Semantic Web Annotations …, 2006
Using the electronic medical record to identify patients at high risk for frequent emergency department visits and high system costs
DW Frost, S Vembu, J Wang, K Tu, Q Morris, HB Abrams
The American journal of medicine 130 (5), 601. e17-601. e22, 2017
RNAcompete-S: Combined RNA sequence/structure preferences for RNA binding proteins derived from a single-step in vitro selection
KB Cook, S Vembu, KCH Ha, H Zheng, KU Laverty, TR Hughes, D Ray, ...
Methods 126, 18-28, 2017
Inhibition in multiclass classification
R Huerta, S Vembu, JM Amigó, T Nowotny, C Elkan
Neural computation 24 (9), 2473-2507, 2012
A self-organizing map based knowledge discovery for music recommendation systems
S Vembu, S Baumann
Computer music modeling and retrieval, Lecture Notes in Computer Science …, 2005
On time series features and kernels for machine olfaction
S Vembu, A Vergara, MK Muezzinoglu, R Huerta
Sensors and Actuators B: Chemical 174, 535-546, 2012
Towards a socio-cultural compatibility of MIR systems
S Baumann, T Pohle, V Shankar
Proceedings of the 5th International Conference of Music Information Retrieval, 2004
On structured output training: Hard cases and an efficient alternative
T Gärtner, S Vembu
Machine learning 76 (2-3), 227-242, 2009
Using error decay prediction to overcome practical issues of deep active learning for named entity recognition
HS Chang, S Vembu, S Mohan, R Uppaal, A McCallum
Machine Learning 109, 1749-1778, 2020
Probabilistic structured predictors
S Vembu, T Gärtner, M Boley
Proceedings of the 25th Conference on Uncertainty in Artificial Intelligence, 2009
Dynamical SVM for time series classification
R Huerta, S Vembu, MK Muezzinoglu, A Vergara
Proceedings of the Joint 34th DAGM and 36th OAGM Symposium, 2012
Interactive learning from multiple noisy labels
S Vembu, S Zilles
Machine Learning and Knowledge Discovery in Databases: European Conference …, 2016
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Articles 1–20