Browsing by Advisor "Narasimha Murty, M"
Now showing items 1-20 of 25
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ACE-Model: A Conceptual Evolutionary Model For Evolutionary Computation And Artificial Life
(Indian Institute of Science, 2005-02-07)Darwinian Evolutionary system - a system satisfying the abstract conditions: reproduction with heritable variation, in a finite world, giving rise to Natural Selection encompasses a complex and subtle system of interrelated ... -
Algorithmic knowledge for a knowledge-based clustering environment.
Clustering is a process of partitioning a given set of objects into meaningful groups. It has potential applications in many areas, including: 1.Image Segmentation, 2. Speech/speaker Classification, 2.ECG Classificati ... -
Data clustering and evolutionary algorithms for data mining
In this work, we present a scheme for selecting optimal prototypes from large data sets, as a part of "Data Mining process". Data mining is defined as a process of non-trivial extraction of implicit, previously unknown and ... -
Efficient Frequent Closed Itemset Algorithms With Applications To Stream Mining And Classification
(2010-08-24)Data mining is an area to find valid, novel, potentially useful, and ultimately understandable abstractions in a data. Frequent itemset mining is one of the important data mining approaches to find those abstractions in ... -
Efficient Kernel Methods For Large Scale Classification
(2011-02-22)Classification algorithms have been widely used in many application domains. Most of these domains deal with massive collection of data and hence demand classification algorithms that scale well with the size of the data ... -
Embedding Networks: Node and Graph Level Representations
Graph neural networks gained significant attention for graph representation and classification in the machine learning community. For graph classification, different pooling techniques are introduced, but none of them has ... -
Hierarchical Data Structures for Pattern Recognition
(Indian Institute of Science, 2005-02-22)Pattern recognition is an important area with potential applications in computer vision, Speech understanding, knowledge engineering, bio-medical data classification, earth sciences, life sciences, economics, psychology, ... -
Intelligent backtracking in logic programming.
Logic programming languages are being used extensively in the Fifth Generation Computer Project and in the areas of artificial intelligence, knowledge representation, expert systems and other reasoning systems. One very ... -
Kernel Methods Fast Algorithms and real life applications
(Indian Institute of Science, 2005-02-08)Support Vector Machines (SVM) have recently gained prominence in the field of machine learning and pattern classification (Vapnik, 1995, Herbrich, 2002, Scholkopf and Smola, 2002). Classification is achieved by finding a ... -
Knowledge-based preanalysis for multilevel clustering
Multilevel clustering offers the cluster analyst flexibility of choosing different algorithms at different levels with the possibility of reducing overall computational effort in comparison with the single-level application ... -
Labelled clustering and its applications
Clustering is a process of grouping a collection of objects. Clustering approaches can be broadly categorized into conventional and knowledge-based approaches. In a conventional approach, objects are typically represented ... -
Large Data Clustering And Classification Schemes For Data Mining
(2009-03-20)Data Mining deals with extracting valid, novel, easily understood by humans, potentially useful and general abstractions from large data. A data is large when number of patterns, number of features per pattern or both are ... -
Learning From Examples Using Hierarchical Counterfactual Expressions
In this study, we develop algorithms for learning concepts from examples. Learning is the capability that allows a system to improve its performance. It involves the ability to correct errors, learn domain knowledge, ... -
Learning subspace methods using weighted and multi-subspace representations
The learning subspace methods (LSMs) of classification are decision-theoretic pattern recognition methods where the primary model for a class is a linear subspace of the Euclidean pattern space. Classification is based on ... -
On Generalized Measures Of Information With Maximum And Minimum Entropy Prescriptions
(2008-01-29)Kullback-Leibler relative-entropy or KL-entropy of P with respect to R defined as ∫xlnddPRdP , where P and R are probability measures on a measurable space (X, ), plays a basic role in the definitions of classical information ... -
Outlier Detection with Applications in Graph Data Mining
(2018-04-24)Outlier detection is an important data mining task due to its applicability in many contemporary applications such as fraud detection and anomaly detection in networks, etc. It assumes significance due to the general ... -
Pattern classification using conjuctive conceptual clustering procedures
This thesis deals with the salient features of the conjunctive conceptual algorithm CLUSTER/2 and describes new algorithms based on conjunctive concepts to overcome some of the problems associated with CLUSTER/2. From a ... -
Pattern representation and prototype selection for handwritten digit recognition
In this work, we present three independent ideas to increase the classification accuracy using the nearest neighbour classifier. These ideas are: (i) combination of decisions using different representation schemes, (ii) ...