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Harish Ragavan

dblp:67/3119 · DBLP profile ↗
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6ranked-venue papers
4as first author
0since 2021 · last 1993
—ORCID · none

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

Artificial intelligence and machine learning · 6 · 4 first-authorGraphics, computer vision, multimedia, augmented reality and games · 3 · 2 first-authorSoftware engineering, systems software and programming languages · 1

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
4 papers
Knowledge representation and reasoning · 43% Representation and self-supervised learning · 41% Deep learning architectures and training · 16%

Topics — the 4 heaviest of 5, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Machine learning › Representation and self-supervised learning › feature transformation
feature construction
0.021993
Lookahead Feature Construction for Learning Hard Concepts · ICML 1993
The Utility of Feature Construcuon for Back-Propagation · IJCAI 1991
Knowledge, reasoning and agents › Knowledge representation and reasoning
concept learning
0.021993
Complex Concept Acquisition through Directed Search and Feature Caching · IJCAI 1993
Lookahead Feature Construction for Learning Hard Concepts · ICML 1993
Machine learning › Deep learning architectures and training
backpropagation
0.011991
The Utility of Feature Construcuon for Back-Propagation · IJCAI 1991
Knowledge, reasoning and agents › Knowledge representation and reasoning
relational learning
0.011991
Relations, Knowledge and Empirical Learning · ML 1991

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

feature caching · 0.0directed search · 0.0feature construction · 0.0empirical learning · 0.0backpropagation · 0.0
YearPublicationVenuePosition
1993 Lookahead Feature Construction for Learning Hard Concepts
Harish Ragavan, Larry A. Rendell
ICML1
1993 Complex Concept Acquisition through Directed Search and Feature Caching
Harish Ragavan, Larry A. Rendell, Michael J. Shaw, Antoinette Tessmer
IJCAI1
1993 Improving the Design of Induction Methods by Analyzing Algorithm Functionality and Data-Based Concept Complexity
Larry A. Rendell, Harish Ragavan
IJCAI2
1992 Knowledge Engineering for Protein Structure and Motifs: Design of a Prototype System
abstract
A knowledge base and learning system is designed to help biologists predict protein structure and function based on sequence information. The knowledge base contains diverse information about: (a) protein motif sequences and structures, (b) heuristics and programs for identifying protein features, and (c) molecular simulation programs. The learning system selects the most relevant information for a particular prediction task and optimally integrates the information to generate accurate and comprehensible hypotheses. Biologists define the objectives for learning such as accuracy and comprehensibility. To overcome the limitations of existing induction algorithms, techniques are developed for constructing new features based on the existing knowledge base. Optimization algorithms are used for determining the best combination of induction and feature construction strategies for a problem.>
Shankar Subramaniam, David K. Tcheng, K. Hu, Harish Ragavan, Larry A. Rendell
SEKE4
1991 Relations, Knowledge and Empirical Learning
Harish Ragavan, Larry A. Rendell
ML1
1991 The Utility of Feature Construcuon for Back-Propagation
Harish Ragavan, Selwyn Piramuthu
IJCAI1