41 citations · 53 across the 9 of their papers we have counts for
7 papers · 1 filter
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…
Model Selection with Nonlinear Embedding for Unsupervised Domain Adaptation
Hemanth Venkateswara, Shayok Chakraborty, Troy McDaniel +1
Domain adaptation deals with adapting classifiers trained on data from a source distribution, to work effectively on data from a target distribution. In this paper, we introduce th…
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…
Nonlinear Embedding Transform for Unsupervised Domain Adaptation
Hemanth Venkateswara, Shayok Chakraborty, Sethuraman Panchanathan
The problem of domain adaptation (DA) deals with adapting classifier models trained on one data distribution to different data distributions. In this paper, we introduce the Nonlin…
Deep Hashing Network for Unsupervised Domain Adaptation
Hemanth Venkateswara, Jose Eusebio, Shayok Chakraborty +1
In recent years, deep neural networks have emerged as a dominant machine learning tool for a wide variety of application domains. However, training a deep neural network requires a…