6 citations · 7 across the 4 of their papers we have counts for
4 papers
Enhash: A Fast Streaming Algorithm For Concept Drift Detection
Aashi Jindal, Prashant Gupta, Debarka Sengupta +1
We propose Enhash, a fast ensemble learner that detects \textit{concept drift} in a data stream. A stream may consist of abrupt, gradual, virtual, or recurring events, or a mixture…
A Weighted Mutual k-Nearest Neighbour for Classification Mining
Joydip Dhar, Ashaya Shukla, Mukul Kumar +1
kNN is a very effective Instance based learning method, and it is easy to implement. Due to heterogeneous nature of data, noises from different possible sources are also widespread…
Guided Random Forest and its application to data approximation
Prashant Gupta, Aashi Jindal, Jayadeva +1
We present a new way of constructing an ensemble classifier, named the Guided Random Forest (GRAF) in the sequel. GRAF extends the idea of building oblique decision trees with loca…
Pentagon at MEDIQA 2019: Multi-task Learning for Filtering and Re-ranking Answers using Language Inference and Question Entailment
Hemant Pugaliya, Karan Saxena, Shefali Garg +4
Parallel deep learning architectures like fine-tuned BERT and MT-DNN, have quickly become the state of the art, bypassing previous deep and shallow learning methods by a large marg…