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P. Sarat Chandran

dblp:64/5662 · DBLP profile ↗
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2ranked-venue papers
2as first author
0since 2021 · last 1994
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Artificial intelligence and machine learning · 1 · 1 first-authorHuman-computer interaction and ubiquitous computing · 1 · 1 first-author

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Artificial intelligence
1 paper
Deep learning architectures and training · 77% Probabilistic and Bayesian machine learning · 23%

Topics — the 2 heaviest of 2, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Machine learning › Deep learning architectures and training › feedforward neural network › feedforward neural network training
multilayer perceptron training		 0.011994
Comments on "Comparative Analysis of Backpropagation and the Extended Kalman Filter for Training Multilayer Perceptrons" · IEEE Trans. Pattern Anal. Mach. Intell. 1994
Machine learning › Probabilistic and Bayesian machine learning › statistical inference › bayesian inference › bayesian filtering › kalman filtering
extended kalman filter		 0.011994
Comments on "Comparative Analysis of Backpropagation and the Extended Kalman Filter for Training Multilayer Perceptrons" · IEEE Trans. Pattern Anal. Mach. Intell. 1994

Methods — techniques the papers use, named apart from their topics

extended kalman filter · 0.0backpropagation · 0.0
YearPublicationVenuePosition
1994 Comments on "Comparative Analysis of Backpropagation and the Extended Kalman Filter for Training Multilayer Perceptrons"
abstract
In this note, the connection between the backpropagation algorithm and the extended Kalman filter is analyzed using an alternate form of representation for the Kalman gain term and shown to be much simpler than that reported by Ruck et al (1992).>
P. Sarat Chandran
 IEEE Trans. Pattern Anal. Mach. Intell.1
1994 A Comment on Noise Injection into Inputs in Backpropagation
abstract
In the above paper (K. Matsuoka, IEEE Trans. SMC, vol. 22, no. 3, pp. 436-440, 1992), the author defines the sensitivity R for a feedforward network and proceeds to derive an expression for R. It is pointed out that an assumption in the equation for R is incorrect. In reply the author accepts that the comment is correct.
P. Sarat Chandran, K. Matsuoka
 IEEE Trans. Syst. Man Cybern. Syst.1