Search Results - "Gupta, Maya R"

Functional Bregman Divergence and Bayesian Estimation of Distributions by Frigyik, B.A., Srivastava, S., Gupta, M.R.

“…A class of distortions termed functional Bregman divergences is defined, which includes squared error and relative entropy. A functional Bregman divergence…”
Get full text

Optimized Regression for Efficient Function Evaluation by Garcia, Eric, Arora, Raman, Gupta, Maya R.

“…In many applications of regression, one is concerned with the efficiency of the estimated function in addition to the accuracy of the regression. For…”
Get full text

Completely Lazy Learning by Garcia, Eric K, Feldman, Sergey, Gupta, Maya R, Srivastava, Santosh

“…Local classifiers are sometimes called lazy learners because they do not train a classifier until presented with a test sample. However, such methods are…”
Get full text

OCR binarization and image pre-processing for searching historical documents by Gupta, Maya R., Jacobson, Nathaniel P., Garcia, Eric K.

“…We consider the problem of document binarization as a pre-processing step for optical character recognition (OCR) for the purpose of keyword search of…”
Get full text

Lymphopenia and Severe Combined Immunodeficiency (SCID) - Think Before You Ink by Aluri, Jahnavi, Gupta, Maya R., Dalvi, Aparna, Mhatre, Snehal, Kulkarni, Manasi, Desai, Mukesh, Shah, Nitin K., Madkaikar, Manisha R.

“…Objectives Severe combined immunodeficiency (SCID) represents one of the most severe forms of Primary immunodeficiency (PID) disorders, characterized by T cell…”
Get full text

Design goals and solutions for display of hyperspectral images by Jacobson, N.P., Gupta, M.R.

“…Design goals and solutions are proposed for the display of hyperspectral imagery on tristimulus displays. The requirements of a hyperspectral visualization…”
Get full text

Adaptive Local Linear Regression With Application to Printer Color Management by Gupta, M.R., Garcia, E.K., Chin, E.

“…Local learning methods, such as local linear regression and nearest neighbor classifiers, base estimates on nearby training samples, neighbors. Usually, the…”
Get full text

Bounds on the Bayes Error Given Moments by Frigyik, Bela A., Gupta, M. R.

“…We show how to compute lower bounds for the supremum Bayes error if the class-conditional distributions must satisfy moment constraints, where the supremum is…”
Get full text

Joint deconvolution and classification with applications to passive acoustic underwater multipath by Anderson, Hyrum S., Gupta, Maya R.

“…This paper addresses the problem of classifying signals that have been corrupted by noise and unknown linear time-invariant (LTI) filtering such as multipath,…”
Get full text

Channel-Robust Classifiers by Anderson, H S, Gupta, M R, Swanson, E, Jamieson, K

“…A key assumption underlying traditional supervised learning algorithms is that labeled examples used to train a classifier are drawn i.i.d. from the same…”
Get full text

Generative models for similarity-based classification by Cazzanti, Luca, Gupta, Maya R., Koppal, Anjali J.

“…A maximum-entropy approach to generative similarity-based classifiers model is proposed. First, a descriptive set of similarity statistics is assumed to be…”
Get full text

Nonparametric supervised learning by linear interpolation with maximum entropy by Gupta, M.R., Gray, R.M., Olshen, R.A.

“…Nonparametric neighborhood methods for learning entail estimation of class conditional probabilities based on relative frequencies of samples that are…”
Get full text

On minimizing distortion and relative entropy by Friedlander, M.P., Gupta, M.R.

“…A common approach for estimating a probability mass function w when given a prior q and moment constraints given by Aw/spl les/b is to minimize the relative…”
Get full text

Training a support vector machine to classify signals in a real environment given clean training data by Jamieson, Kevin, Gupta, Maya R, Swanson, Eric, Anderson, Hyrum S

“…When building a classifier from clean training data for a particular test environment, knowledge about the environmental noise and channel should be taken into…”
Get full text

Bayesian and pairwise local similarity discriminant analysis by Sadowski, Peter, Cazzanti, Luca, Gupta, Maya R

“…We investigate three extensions to the generative similarity-based classifier called local similarity discriminant analysis (local SDA): a Bayesian approach to…”
Get full text

Linear Fusion of Image Sets for Display by Jacobson, N.P., Gupta, M.R., Cole, J.B.

“…Many remote-sensing applications produce large sets of images, such as hyperspectral images or time-indexed image sequences. We explore methods to display such…”
Get full text

A Quasi EM Method for Estimating Multiple Transmitter Locations by Nelson, J.K., Gupta, M.R., Almodovar, J.E., Mortensen, W.H.

“…We consider estimating multiple transmitter locations based on received signal strength measurements by a sensor network of randomly located receivers. This…”
Get full text

Reliable early classification of time series by Anderson, H. S., Parrish, N., Tsukida, K., Gupta, M. R.

“…Early classification of time series is important in time-sensitive applications. An approach is presented for early classification using generative classifiers…”
Get full text

Contact clustering and classification using likelihood-based similarities by Hanusa, E., Gupta, M. R., Krout, D. W.

“…This paper presents the results of using a likelihood-based clustering step before tracking on a multistatic sonar step. The likelihood-based clustering…”
Get full text

Wavelet Principal Component Analysis and its Application to Hyperspectral Images by Gupta, M. R., Jacobson, N. P.

“…We investigate reducing the dimensionality of image sets by using principal component analysis on wavelet coefficients to maximize edge energy in the reduced…”
Get full text