Narasimha M. Murty

Partition based pattern synthesis technique with efficient algorithms for nearest neighbor classification (2006)

Hybrid hierarchical clustering techniques which combine the characteristics of different partitional clustering techniques or partitional and hierarchical clustering techniques are interesting. In...

Viswanath, P, Murty, Narasimha M, Bhatnagar, Shalabh

Nearest neighbor (NN) classifier is a popular non-parametric classifier. It is conceptually a simple classifier and shows good performance. Due to the curse of dimensionality effect, the size of...

Efficient median based clustering and classification techniques for protein sequences (2006)

Rough set based incremental clustering of interval data (2006)

In this paper, an efficient K-medians clustering (unsupervised) algorithm for prototype selection and Supervised K-medians (SKM) classification technique for protein sequences are presented. For...

Asharaf, S, Murty, Narasimha M, Shevade, SK

This paper introduces a novel incremental approach to clustering interval data. The method employs rough set theory to capture the inherent uncertainty involved in cluster analysis. Our experimental...

Nonextensive triangle equality and other properties of Tsallis relative-entropy minimization (2006)

Dukkipati, Ambedkar, Murty, Narasimha M, Bhatnagar, Shalabh

Kullback-Leibler relative-entropy has unique properties in cases involving distributions resulting from relative-entropy minimization. Tsallis relative-entropy is a one-parameter generalization of...

Scalable Rough Support Vector Clustering (2006)

Rough set based incremental clustering of interval data (2006)

In this paper a novel scalable soft support vector clustering algorithm is proposed. Here softness is imparted to Support Vector Clustering paradigm by employing rough set theory and scalability is...

Asharaf, S, Murty, Narasimha M, Shevade, SK

This paper introduces a novel incremental approach to clustering interval data. The method employs rough set theory to capture the inherent uncertainty involved in cluster analysis. Our experimental...

Partition based pattern synthesis technique with efficient algorithms for nearest neighbor classification (2006)

Viswanath, P, Murty, Narasimha M, Bhatnagar, Shalabh

Nearest neighbor (NN) classifier is a popular non-parametric classifier. It is conceptually a simple classifier and shows good performance. Due to the curse of dimensionality effect, the size of...

Efficient median based clustering and classification techniques for protein sequences (2006)

Efficient bottom-up hybrid hierarchical clustering techniques for protein sequence classification (2006)

In this paper, an efficient K-medians clustering (unsupervised) algorithm for prototype selection and Supervised K-medians (SKM) classification technique for protein sequences are presented. For...

Scalable Rough Support Vector Clustering (2006)

Hybrid hierarchical clustering techniques which combine the characteristics of different partitional clustering techniques or partitional and hierarchical clustering techniques are interesting. In...

Efficient pattern synthesis for nearest neighbour classifier (2005)

In this paper a novel scalable soft support vector clustering algorithm is proposed. Here softness is imparted to Support Vector Clustering paradigm by employing rough set theory and scalability is...

Agrawal, Monu, Gupta, Neha, Shreelekshmi, R, Murty, Narasimha M

Synthetic pattern generation is one of the strategies to overcome the curse of dimensionality, but it has its own drawbacks. Most of the synthetic pattern generation techniques take more time than...

Rough support vector clustering (2005)

Overlap pattern synthesis with an efficient nearest neighbor classifier (2005)

In this paper a novel kernel-based soft clustering method is proposed. This method incorporates rough set theoretic ﬂavour in support vector clustering paradigm to achieve soft clustering....

Viswanath, P, Murty, Narasimha M, Bhatnagar, Shalabh

Nearest neighbor (NN) classifier is the most popular non-parametric classifier. It is a simple classifier with no design phase and shows good performance. Important factors affecting the efficiency...

Overlap pattern synthesis with an efficient nearest neighbor classifier (2005)

Viswanath, P, Murty, Narasimha M, Bhatnagar, Shalabh

Nearest neighbor (NN) classifier is the most popular non-parametric classifier. It is a simple classifier with no design phase and shows good performance. Important factors affecting the efficiency...

Efficient pattern synthesis for nearest neighbour classifier (2005)

Agrawal, Monu, Gupta, Neha, Shreelekshmi, R, Murty, Narasimha M

Synthetic pattern generation is one of the strategies to overcome the curse of dimensionality, but it has its own drawbacks. Most of the synthetic pattern generation techniques take more time than...

Rough support vector clustering (2005)

Information theoretic justification of Boltzmann selection and its generalization to Tsallis case (2005)

In this paper a novel kernel-based soft clustering method is proposed. This method incorporates rough set theoretic ﬂavour in support vector clustering paradigm to achieve soft clustering....

Dukkipati, Ambedkar, Murty, Narasimha M, Bhatnagar, Shalabh

A generalized evolutionary algorithm based on Tsallis statistics is proposed. The algorithm uses Tsallis generalized canonical distribution, which is one parameter generalization of Boltzmann...

Information theoretic justification of Boltzmann selection and its generalization to Tsallis case (2005)

Dukkipati, Ambedkar, Murty, Narasimha M, Bhatnagar, Shalabh

A generalized evolutionary algorithm based on Tsallis statistics is proposed. The algorithm uses Tsallis generalized canonical distribution, which is one parameter generalization of Boltzmann...

A rough fuzzy approach to web usage categorization (2004)

Asharaf, S, Murty, Narasimha M

This paper introduces a novel clustering scheme employing a combination of rough set theory and fuzzy set theory to generate meaningful abstractions from web access logs. Our experimental results...

Leaders-Subleaders: An efficient hierarchical clustering algorithm for large data sets (2004)

A rough fuzzy approach to web usage categorization (2004)

In this paper, an efficient hierarchical clustering algorithm, suitable for large data sets is proposed for effective clustering and prototype selection for pattern classification. It is another...

Asharaf, S, Murty, Narasimha M

This paper introduces a novel clustering scheme employing a combination of rough set theory and fuzzy set theory to generate meaningful abstractions from web access logs. Our experimental results...

Leaders-Subleaders: An efficient hierarchical clustering algorithm for large data sets (2004)

Hybrid Learning Scheme for Data Mining Applications (2004)

In this paper, an efficient hierarchical clustering algorithm, suitable for large data sets is proposed for effective clustering and prototype selection for pattern classification. It is another...

Adaptive Boosting with Leader Based Learners for Classification of Large Handwritten Data (2004)

Classification of large datasets is a challenging task in data mining. In the current work, we propose a novel method that compresses the data and classifies the test data directly in its compressed...

An Efficient Technique for Protein Sequence Clustering and Classification (2004)

Boosting is a general method for improving the accuracy of a learning algorithm. AdaBoost, short form for adaptive boosting method, consists of repeated use of a weak or a base learning algorithm to...

Cauchy Annealing Schedule: An Annealing Schedule for Boltzmann Selection Scheme in Evolutionary Algorithms (2004)

In this paper, a technique to reduce time and space during protein sequence clustering and classification is presented. During training and testing phase, the similarity score value between a pair of...

Dukkipati, Ambedkar, Murty, Narasimha M, Bhatnagar, Shalabh

Boltzmann selection is an important selection mechanism in evolutionary algorithms as it has theoretical properties which help in theoretical analysis. However, Boltzmann selection is not used in...

A Pattern Synthesis Technique with an Efficient Nearest Neighbor Classifier for Binary Pattern Recognition (2004)

Viswanath, P, Murty, Narasimha M, Bhatnagar, Shalabh

Important factors affecting the efficiency and performance of the nearest neighbor classifier (NNC) are space, classification time requirements and for high dimensional data, due to the curse of...

Hybrid Learning Scheme for Data Mining Applications (2004)

Adaptive Boosting with Leader Based Learners for Classification of Large Handwritten Data (2004)

Classification of large datasets is a challenging task in data mining. In the current work, we propose a novel method that compresses the data and classifies the test data directly in its compressed...

An Efficient Technique for Protein Sequence Clustering and Classification (2004)

Boosting is a general method for improving the accuracy of a learning algorithm. AdaBoost, short form for adaptive boosting method, consists of repeated use of a weak or a base learning algorithm to...

Cauchy Annealing Schedule: An Annealing Schedule for Boltzmann Selection Scheme in Evolutionary Algorithms (2004)

In this paper, a technique to reduce time and space during protein sequence clustering and classification is presented. During training and testing phase, the similarity score value between a pair of...

Dukkipati, Ambedkar, Murty, Narasimha M, Bhatnagar, Shalabh

Boltzmann selection is an important selection mechanism in evolutionary algorithms as it has theoretical properties which help in theoretical analysis. However, Boltzmann selection is not used in...

A Pattern Synthesis Technique with an Efficient Nearest Neighbor Classifier for Binary Pattern Recognition (2004)

Viswanath, P, Murty, Narasimha M, Bhatnagar, Shalabh

Important factors affecting the efficiency and performance of the nearest neighbor classifier (NNC) are space, classification time requirements and for high dimensional data, due to the curse of...

Knowledge-based association rule mining using AND–OR taxonomies (2003)

Subramanian, DK, Ananthanarayana, VS, Murty, Narasimha M

We introduce a knowledge-based approach to mine generalized association rules which is sound and interactive. Proposed mining is sound because our scheme uses knowledge for mining for only those...

Quotient Evolutionary Space: Abstraction of Evolutionary process w.r.t macroscopic properties (2003)

Dukkipati, Ambedkar, Murty, Narasimha M, Bhatnagar, Shalabh

Darwinian evolution, which is characterized in terms of particular macroscopic behavior that emerges from microscopic organismic interaction, considers populations as units of evolutionary change. We...

An efficient incremental protein sequence clustering algorithm (2003)

Knowledge-based association rule mining using AND–OR taxonomies (2003)

Clustering is the division of data into groups of similar objects. The main objective of this unsupervised learning technique is to find a natural grouping or meaningful partition by using a distance...

Subramanian, DK, Ananthanarayana, VS, Murty, Narasimha M

We introduce a knowledge-based approach to mine generalized association rules which is sound and interactive. Proposed mining is sound because our scheme uses knowledge for mining for only those...

Quotient Evolutionary Space: Abstraction of Evolutionary process w.r.t macroscopic properties (2003)

Dukkipati, Ambedkar, Murty, Narasimha M, Bhatnagar, Shalabh

Darwinian evolution, which is characterized in terms of particular macroscopic behavior that emerges from microscopic organismic interaction, considers populations as units of evolutionary change. We...

An efficient incremental protein sequence clustering algorithm (2003)

An Incremental Prototype Set Building Technique (2002)

Clustering is the division of data into groups of similar objects. The main objective of this unsupervised learning technique is to find a natural grouping or meaningful partition by using a distance...

Sushella, Devi V, Murty, Narasimha M

This paper deals with the task of finding a set of prototypes from the training set. A reduced set is obtained which is used instead of the training set when nearest neighbour classification is used....

Kernel Enabled K-Means Algorithm (2002)

An efficient incremental mining algorithm for compact realization of prototypes (2002)

We present a novel method to learn arbitrary cluster boundaries by extending the k-means algorithm to use Mercer kernels. We inter- pret each cluster centroid as a linear com- bination of the cluster...

Viswanath, P, Murty, Narasimha M

There are two phases in pattern classi viz design phase (abstractions are created/learnt), classi phase (abstractions are used to classify a test pattern). Classi based on neural networks and genetic...

Use of Multi Category Proximal SVM for Dataset Reduction (2002)

Adapting Question Answering Techniques to the Web (2002)

We present a tutorial introduction to Support Vector Machines (SVM) and try to show, using intuitive arguments, why SVM's tend to perform so well on a variety of challenging problems. We then discuss...

Parikh, Jignashu, Murty, Narasimha M

The Web has emerged as a huge information repository that can be used for various knowledge based applications, an important one being question answering (QA). The paper discusses the issues involved...

Geometric SVM: A Fast and Intuitive SVM Algorithm (2002)

SSVM: A Simple SVM Algorithm (2002)

We present a geometrically motivated algorithm for finding the Support Vectors of a given set of points. This algorithm is reminiscent of the DirectSVM algorithm, in the way it picks data points for...

Vishwanathan, SVM, Murty, Narasimha M

We present a fast iterative algorithm for identifying the support vectors of a given set of points. Our algorithm works by maintaining a candidate support vector set. It uses a greedy approach to...

Selection by Parts: 'Selection in Two Episodes' in Evolutionary Algorithms (2002)

Dukkipati, Ambedkar, Murty, Narasimha M

Natural selection is the central concept of Darwinian evolution and hence selection is central for evolutionary computation. Naive models of evolution define natural selection as a process which...

An Incremental Prototype Set Building Technique (2002)

Sushella, Devi V, Murty, Narasimha M

This paper deals with the task of finding a set of prototypes from the training set. A reduced set is obtained which is used instead of the training set when nearest neighbour classification is used....

Kernel Enabled K-Means Algorithm (2002)

An efficient incremental mining algorithm for compact realization of prototypes (2002)

We present a novel method to learn arbitrary cluster boundaries by extending the k-means algorithm to use Mercer kernels. We inter- pret each cluster centroid as a linear com- bination of the cluster...

Viswanath, P, Murty, Narasimha M

There are two phases in pattern classi viz design phase (abstractions are created/learnt), classi phase (abstractions are used to classify a test pattern). Classi based on neural networks and genetic...

Use of Multi Category Proximal SVM for Dataset Reduction (2002)

Adapting Question Answering Techniques to the Web (2002)

We present a tutorial introduction to Support Vector Machines (SVM) and try to show, using intuitive arguments, why SVM's tend to perform so well on a variety of challenging problems. We then discuss...

Parikh, Jignashu, Murty, Narasimha M

The Web has emerged as a huge information repository that can be used for various knowledge based applications, an important one being question answering (QA). The paper discusses the issues involved...

Geometric SVM: A Fast and Intuitive SVM Algorithm (2002)

SSVM: A Simple SVM Algorithm (2002)

We present a geometrically motivated algorithm for finding the Support Vectors of a given set of points. This algorithm is reminiscent of the DirectSVM algorithm, in the way it picks data points for...

Vishwanathan, SVM, Murty, Narasimha M

We present a fast iterative algorithm for identifying the support vectors of a given set of points. Our algorithm works by maintaining a candidate support vector set. It uses a greedy approach to...

Selection by Parts: 'Selection in Two Episodes' in Evolutionary Algorithms (2002)

Dukkipati, Ambedkar, Murty, Narasimha M

Natural selection is the central concept of Darwinian evolution and hence selection is central for evolutionary computation. Naive models of evolution define natural selection as a process which...

Efficient clustering of large data sets (2001)

Ananthanarayana, VS, Murty, Narasimha M, Subramanian, DK

Clustering is an activity of finding abstractions from data and these abstractions can be used for decision making [1]. In this paper, we select the cluster representatives as prototypes for...

Bootstrapping for efficient handwritten digit recognition (2001)

Saradhi, Vijaya V, Murty, Narasimha M

In this paper we present two algorithms for selecting prototypes from the given training data set. Here, we employ the bootstrap technique to preprocess the data. We compare the proposed algorithms...

Efficient clustering of large data sets (2001)

Ananthanarayana, VS, Murty, Narasimha M, Subramanian, DK

Clustering is an activity of finding abstractions from data and these abstractions can be used for decision making [1]. In this paper, we select the cluster representatives as prototypes for...

Kohonen's SOM with cache (2000)

Stochastic search techniques for post-fault restoration of electrical distribution systems (2000)

The Kohonen self-organizing map (SOM),is a topology-preserving map that maps data from higher dimensions onto a (typically) two-dimensional grid of lattice points [1]. The aim of self-organization is...

Devi, Susheela V, Murty, Narasimha M

Three stochastic search techniques have been used to find the optimal sequence of operations required to restore supply in an electrical distribution system on the occurrence of a fault. The three...

A Stochastic Connectionist Approach for Global Optimization with Application to Pattern Clustering (2000)

Babu, Phanendra G, Murty, Narasimha M, Keerthi, Sathiya S

In this paper, a stochastic connectionist approach is proposed for solving function optimization problems with real-valued parameters. With the assumption of increased processing capability of a node...

Stochastic search techniques for post-fault restoration of electrical distribution systems (2000)

Devi, Susheela V, Murty, Narasimha M

Three stochastic search techniques have been used to find the optimal sequence of operations required to restore supply in an electrical distribution system on the occurrence of a fault. The three...

A Stochastic Connectionist Approach for Global Optimization with Application to Pattern Clustering (2000)

Babu, Phanendra G, Murty, Narasimha M, Keerthi, Sathiya S

In this paper, a stochastic connectionist approach is proposed for solving function optimization problems with real-valued parameters. With the assumption of increased processing capability of a node...

On the Scalability of Genetic Algorithms to Very Large-Scale Feature Selection (2000)

Moser, Andreas, Murty, Narasimha M

Feature Selection is a very promising optimisation strategy for Pattern Recognition systems. But, as an NP-complete task, it is extremely difficult to carry out. Past studies therefore were rather...

Scalable, Distributed and Dynamic Mining of Association Rules (2000)

Ananthanarayana, VS, Subramanian, DK, Murty, Narasimha M

We propose a novel pattern tree called Pattern Count tree (PC- tree) which is a complete and compact representation of the database. We show that construction of this tree and then generation of all...

Kohonen's SOM with cache (2000)

On the Scalability of Genetic Algorithms to Very Large-Scale Feature Selection (2000)

The Kohonen self-organizing map (SOM),is a topology-preserving map that maps data from higher dimensions onto a (typically) two-dimensional grid of lattice points [1]. The aim of self-organization is...

Moser, Andreas, Murty, Narasimha M

Feature Selection is a very promising optimisation strategy for Pattern Recognition systems. But, as an NP-complete task, it is extremely difficult to carry out. Past studies therefore were rather...

Scalable, Distributed and Dynamic Mining of Association Rules (2000)

Ananthanarayana, VS, Subramanian, DK, Murty, Narasimha M

We propose a novel pattern tree called Pattern Count tree (PC- tree) which is a complete and compact representation of the database. We show that construction of this tree and then generation of all...

Genetic K-Means Algorithm (1999)

Krishna, K, Murty, Narasimha M

In this paper, we propose a novel hybrid genetic algorithm (GA) that finds a globally optimal partition of a given data into a specified number of clusters. GAs used earlier in clustering employ...

Genetic K-Means Algorithm (1999)

Krishna, K, Murty, Narasimha M

In this paper, we propose a novel hybrid genetic algorithm (GA) that finds a globally optimal partition of a given data into a specified number of clusters. GAs used earlier in clustering employ...

Growing Subspace Pattern Recognition Methods and Their Neural-Network Models (1997)

Hebbian learning subspace method: A new approach (1997)

In statistical pattern recognition, the decision of which features to use is usually left to human judgment. If possible, automatic methods are desirable. Like multilayer perceptrons, learning...

Growing Subspace Pattern Recognition Methods and Their Neural-Network Models (1997)

In this paper, we propose a new learning (SPRM) called the Hebbian Learning Subspace Method (HLSM). It uses the notion of a weighted squared orthogonal projection distance winch gives different...

Hebbian learning subspace method: A new approach (1997)

In statistical pattern recognition, the decision of which features to use is usually left to human judgment. If possible, automatic methods are desirable. Like multilayer perceptrons, learning...

Extended subspace methods of pattern recognition (1996)

In this paper, we propose a new learning (SPRM) called the Hebbian Learning Subspace Method (HLSM). It uses the notion of a weighted squared orthogonal projection distance winch gives different...

Extended subspace methods of pattern recognition (1996)

The Subspace Pattern Recognition Method (SPRM) is a statistical method where each class is represented by a separate subspace. There are a number of variants to it including the Averaged Learning...

A genetic approach for selection of (near-) optimal subsets of principal components for discrimination (1995)

The Subspace Pattern Recognition Method (SPRM) is a statistical method where each class is represented by a separate subspace. There are a number of variants to it including the Averaged Learning...

Optimal thresholding using multi-state stochastic connectionist approach (1995)

Principal Component Analysis (PCA) is being used both in the preprocessor to a feed-forward neural network and in the Subspace Pattern Recognition Method (SPRM). Most of the classifiers based on PCA...

Optimal thresholding using multi-state stochastic connectionist approach (1995)

In this paper, we describe the applicability of the K-means clustering algorithm for locating thresholds in a given histogram. In order to find optimal thresholds a probabilistic method called...

A genetic approach for selection of (near-) optimal subsets of principal components for discrimination (1995)

In this paper, we describe the applicability of the K-means clustering algorithm for locating thresholds in a given histogram. In order to find optimal thresholds a probabilistic method called...

Knowledge-based clustering approach for data abstraction (1994)

Principal Component Analysis (PCA) is being used both in the preprocessor to a feed-forward neural network and in the Subspace Pattern Recognition Method (SPRM). Most of the classifiers based on PCA...

Clustering with evolution strategies (1994)

Clustering techniques have been used for data abstraction. Data abstraction has many applications in the context of data- bases. Conceptual models are used to bridge the gap between the user's view...

Knowledge-based clustering approach for data abstraction (1994)

Tbe applicability of evolution strategies (ESs), population based stochastic optimization techniques, to optimize clustering objective functions is explored. Clustering objective functions are...

Clustering with evolution strategies (1994)

Clustering techniques have been used for data abstraction. Data abstraction has many applications in the context of data- bases. Conceptual models are used to bridge the gap between the user's view...

A Probabilistic Neural Network for Designing Good Codes (1993)

Tbe applicability of evolution strategies (ESs), population based stochastic optimization techniques, to optimize clustering objective functions is explored. Clustering objective functions are...

A Probabilistic Neural Network for Designing Good Codes (1993)

Designing good error-correcting codes typically requires searching in search spaces. The vastness of search space precludes the use of brute force techniques such as exhaustive enumeration. The...

A knowledge-based clustering algorithm (1991)

Designing good error-correcting codes typically requires searching in search spaces. The vastness of search space precludes the use of brute force techniques such as exhaustive enumeration. The...

We describe a clustering technique that can exploit a large body of knowledge. An algorithm to cluster input objects, using the knowledge available, is presented. This algorithm is order-independent...

In the Hopfield model of content addressable memory, the number of spurious attractors is exponential in the dimensionality of the memory. Hence it is highly likely that the system converges to a...

A New Data Structure HC-Expression for Learning from Examples (1991)

In distributed representations, an object is represented as a pattern of activation over a number of units. Not all patterns of activity are valid in a given context. In case of the Hopfield model of...

Chitoor, Suresh S, Murty, Narasimha M, Bhandaru, Malini K

A new data structure Hierarchical Counterfactual Expression (HC-Expression) is proposed.Its use in the area of learning from examples is studied. HC-Expression is a tree-like structure with alternate...

Incremental Learning from Examples Using HC-Expressions (1991)

Bhandaru, Malini K, Murty, Narasimha M

An incremental learning algorithm for learning from examples using the data structure Hierarchical Counterfactual (HC) expression is presented. The data structure used, Hierarchical Counterfactual...

Clustering Algorithms for Library Comparison (1991)

A knowledge-based clustering algorithm (1991)

Database comparison is an important area of research. Comparison of databases is at two levels: data level and structure level. In this paper, we investigate the applicability of the conventional...

We describe a clustering technique that can exploit a large body of knowledge. An algorithm to cluster input objects, using the knowledge available, is presented. This algorithm is order-independent...

In the Hopfield model of content addressable memory, the number of spurious attractors is exponential in the dimensionality of the memory. Hence it is highly likely that the system converges to a...

Clustering Algorithms for Library Comparison (1991)

In distributed representations, an object is represented as a pattern of activation over a number of units. Not all patterns of activity are valid in a given context. In case of the Hopfield model of...

A New Data Structure HC-Expression for Learning from Examples (1991)

Database comparison is an important area of research. Comparison of databases is at two levels: data level and structure level. In this paper, we investigate the applicability of the conventional...

Chitoor, Suresh S, Murty, Narasimha M, Bhandaru, Malini K

A new data structure Hierarchical Counterfactual Expression (HC-Expression) is proposed.Its use in the area of learning from examples is studied. HC-Expression is a tree-like structure with alternate...

Incremental Learning from Examples Using HC-Expressions (1991)

Bhandaru, Malini K, Murty, Narasimha M

An incremental learning algorithm for learning from examples using the data structure Hierarchical Counterfactual (HC) expression is presented. The data structure used, Hierarchical Counterfactual...

A plausible acquisition-model for semantic synthesis of patterns (1991)

A division scheme for constructing minimal spanning trees in coordinate space (1990)

The paper deals with a specific knowledge-based approach to concept-synthesis, namely, `function-acquisition'. Comparison between two types of isomorphisms, and these are used in stipulating the...

Choudhury, Sabyasachy, Murty, Narasimha M

An algorithm to generate a minimal spanning tree is presented when the nodes with their coordinates in some m-dimensional Euclidean space and the corresponding metric are given. This algorithm is...

A plausible acquisition-model for semantic synthesis of patterns (1990)

The function-acquisition paradigm in a knowledge-based concept-synthesis environment (1990)

The paper deals with a specific knowledge-based approach to concept-synthesis, namely, `function-acquisition'. Comparison between two types of isomorphisms, and these are used in stipulating the...

Minimal models and real-world models (1990)

The authors consider a knowledge-based approach to concept-synthesis. The notion of concept is examined. Three different approaches to the synthesis of knowledge-based concepts are outlined. One of...

A division scheme for constructing minimal spanning trees in coordinate space (1990)

Proposes a modified predicate completion scheme called the partial predicate completion scheme. It is more general than the delimited predicate completion scheme. The authors also present a scheme...

Choudhury, Sabyasachy, Murty, Narasimha M

An algorithm to generate a minimal spanning tree is presented when the nodes with their coordinates in some m-dimensional Euclidean space and the corresponding metric are given. This algorithm is...

The function-acquisition paradigm in a knowledge-based concept-synthesis environment (1990)

Minimal models and real-world models (1990)

The authors consider a knowledge-based approach to concept-synthesis. The notion of concept is examined. Three different approaches to the synthesis of knowledge-based concepts are outlined. One of...

A Logical Model for Decision-Making (1989)

Proposes a modified predicate completion scheme called the partial predicate completion scheme. It is more general than the delimited predicate completion scheme. The authors also present a scheme...

A Logical Model for Decision-Making (1989)

The general form of a decision making model raises hypotheses about the dynamics of stimulus, conception, and response and we might suppose that the decision process begins with the perception of...