7 citations · 11 across the 5 of their papers we have counts for
5 papers
Model adaptation and unsupervised learning with non-stationary batch data under smooth concept drift
Subhro Das, Prasanth Lade, Soundar Srinivasan
Most predictive models assume that training and test data are generated from a stationary process. However, this assumption does not hold true in practice. In this paper, we consid…
Efficient Approximate Solutions to Mutual Information Based Global Feature Selection
Hemanth Venkateswara, Prasanth Lade, Binbin Lin +2
Mutual Information (MI) is often used for feature selection when developing classifier models. Estimating the MI for a subset of features is often intractable. We demonstrate, that…
Multiresolution Match Kernels for Gesture Video Classification
Hemanth Venkateswara, Vineeth N. Balasubramanian, Prasanth Lade +1
The emergence of depth imaging technologies like the Microsoft Kinect has renewed interest in computational methods for gesture classification based on videos. For several years no…
Coupled Support Vector Machines for Supervised Domain Adaptation
Hemanth Venkateswara, Prasanth Lade, Jieping Ye +1
Popular domain adaptation (DA) techniques learn a classifier for the target domain by sampling relevant data points from the source and combining it with the target data. We presen…
Causal Discovery for Manufacturing Domains
Katerina Marazopoulou, Rumi Ghosh, Prasanth Lade +1
Yield and quality improvement is of paramount importance to any manufacturing company. One of the ways of improving yield is through discovery of the root causal factors affecting…