<p>Shiv Nadar University is a comprehensive, multidisciplinary, research-focused, and student-centric University offering a full range of academic programs at the undergraduate, postgraduate and doctoral level. With state-of-the-art infrastructure, the University comprises of academic wings, sophisticated labs, international standard sports facilities, amphitheaters, auditorium, conference rooms and smart classrooms. The University&rsquo;s goal is to become internationally recognized for the quality of its research and creative endeavors and their applicability to improving quality of life, generating new insights and expanding the boundaries of human knowledge creativity. Committed to excellence in teaching, research and service, the University aims to serve the higher education needs of India and the world beyond.</p>

Shiv Nadar University

This article addresses the problem of adaptive control of nonlinear chemical processes with time-varying dynamics. The Q-learning and policy-iteration algorithms have been considered in the reinforcement learning (RL) framework. The performance of the two algorithms has been tested on highly nonlinear simulated continuous stirred tank reactor (CSTR). Comparison with conventional methods shows that the RL techniques are able to achieve better performance, and robustness against uncertainties. The policy-iteration algorithm is relatively faster in convergence, and better in robustness. Copyright © 2010 Curtin University of Technology and John Wiley & Sons, Ltd.

World University root node

Electrical Engineering

(SoE) School of Engineering

Asia-Pacific Journal of Chemical Engineering

Reinforcement learning framework for adaptive control of nonlinear chemical processes

We present an improved version of one-against-all method for multiclass SVM classification based on subset sample selection, named reduced one-against-all, to achieve high performance in large multiclass problems. Reduced one-against-all drastically decreases the computing effort involved in training one-against-all classifiers, without any compromise in classification accuracy. Computational comparisons on publicly available datasets indicate that the proposed method has comparable accuracy to that of conventional one-against-all method, but with an order of magnitude faster. On the largest dataset considered, reduced one-against-all method achieved 50% reduction in computing time over one-against-all method for almost the same classification accuracy. We further investigated reduced one-against-all with linear kernel for multi-label text categorization applications. Computational results demonstrate the effectiveness of the proposed method on both the text corpuses considered. © 2011 Elsevier Ltd. All rights reserved.

Expert Systems with Applications

Reduced one-against-all method for multiclass SVM classification

In this paper, we propose a novel framework for segmentation of documents with complex layouts. The document segmentation is performed by combination of clustering and conditional random fields (CRF) based modeling. The bottom-up approach for segmentation assigns each pixel to a cluster plane based on color intensity. A CRF based discriminative model is learned to extract the local neighborhood information in different cluster/color planes. The final category assignment is done by a top-level CRF based on the semantic correlation learned across clusters. The proposed framework has been extensively tested on multi-colored document images with text overlapping graphics/image. © 2011 IEEE.

Proceedings of the International Conference on Document Analysis and Recognition, ICDAR

A CRF based scheme for overlapping multi-colored text graphics separation

Indexing and retrieval performance over digitized document collection significantly depends on the performance of available Optical Character Recognition (OCR). The paper presents a novel document indexing framework which attends the document digitization errors in the indexing process to improve the overall retrieval accuracy. The proposed indexing framework is based on topic modeling using Latent Dirichlet Allocation (LDA). The OCR's confidence in correctly recognizing a symbol is propagated in topic learning process such that semantic grouping of word examples carefully distinguishes between commonly confusing words. We present a novel application of Lucene with topic modeling for document indexing application. The experimental evaluation of the proposed framework is presented on document collection belonging to Devanagari script. © 2011 IEEE.

Searching OCR'ed text: An LDA based approach

The paper presents a novel approach for event detection in sports videos by topic based graphical model learning. The characteristics features defining various sport events are extracted by contextual grouping of low-level video and audio features using topic modeling. Event detection is performed by learning the structure of context based distribution of characteristic features by CRF based graphical model. Experimental evaluation of the proposed concept is presented on recorded video of Handball and Soccer game. © 2011 IEEE.

Proceedings of the IEEE International Conference on Computer Vision

A hybrid framework for event detection using multi-modal features

We present a novel dance posture based annotation model by combining features using Multiple Kernel Learning (MKL). We have proposed a novel feature representation which represents the local texture properties of the image. The annotation model is defined in the direct acyclic graph structure using the binary MKL algorithm. The bag-of-words model is applied for image representation. The experiments have been performed on the image collection belonging to two Indian classical dances (Bharatnatyam and Odissi). The annotation model has been tested using SIFT and the proposed feature individually and by optimally combining both the features. The experiments have shown promising results. © 2011 IEEE.

Proceedings - 2011 3rd National Conference on Computer Vision, Pattern Recognition, Image Processing and Graphics, NCVPRIPG 2011

Annotating dance posture images using multi kernel feature combination

Script identification in a multi-lingual document environ- ment has numerous applications in the field of document image analysis, such as indexing and retrieval or as an initial step towards optical character recognition. In this paper, we propose a novel hierarchical framework for script identification in bi-lingual documents. The framework presents a top-down approach by performing page, block/paragraph and word level script identification in multiple stages. We utilize texture and shape based information embedded in the documents at different levels for feature extraction. The prediction task at different levels of hierarchy is performed by Support Vector Machine (SVM) and Rejection based classifier defined using AdaBoost. Experimental evaluation of the proposed concept on document collections of Hindi/English and Bangla/English scripts have shown promising results. Copyright © 2011 ACM.

ACM International Conference Proceeding Series

Script based text identification: A multi-level architecture

We have proposed a hybrid SVM based decision tree to speedup SVMs in its testing phase for binary classification tasks. While most existing methods addressed towards this task aim at reducing the number of support vectors, we have focused on reducing the number of test datapoints that need SVM's help in getting classified. The central idea is to approximate the decision boundary of SVM using decision trees. The resulting tree is a hybrid tree in the sense that it has both univariate and multivariate (SVM) nodes. The hybrid tree takes SVM's help only in classifying crucial datapoints lying near decision boundary; remaining less crucial datapoints are classified by fast univariate nodes. The classification accuracy of the hybrid tree is guaranteed by tuning a threshold parameter. Extensive computational comparisons on 19 publicly available datasets indicate that the proposed method achieves significant speedup when compared to SVMs, without any compromise in classification accuracy. © 2010 Elsevier Ltd. ALL rights reserved.

Pattern Recognition

A hybrid SVM based decision tree

Linear Support Vector Machines (SVMs) have been used successfully to classify text documents into set of concepts. With the increasing number of linear SVM formulations and decomposition algorithms publicly available, this paper performs a study on their efficiency and efficacy for text categorization tasks. Eight publicly available implementations are investigated in terms of Break Even Point (BEP), F1 measure, ROC plots, learning speed and sensitivity to penalty parameter, based on the experimental results on two benchmark text corpuses. The results show that out of the eight implementations, SVM lin and Proximal SVM perform better in terms of consistent performance and reduced training time. However being an extremely simple algorithm with training time independent of the penalty parameter and the category for which training is being done, Proximal SVM is appealing. We further investigated fuzzy proximal SVM on both the text corpuses; it showed improved generalization over proximal SVM. © 2010 IEEE.

ICMLC 2010 - The 2nd International Conference on Machine Learning and Computing

An investigation on linear SVM and its variants for text categorization

This paper presents a linear SVM (Support Vector Machine) Pyramidal Tree (SVMPT) for binary classification tasks. SVMPT is a modified version of SVM based Tree Type Neural Networks (SVMTNN), reported earlier in the literature [1]. Both the algorithms use parameter-less SVM proposed by Mangasarian [2] for learning in each node. While SVMTNN insists on 100 percent training accuracy, linear SVMPT uses predetermined threshold value to determine when to stop adding new nodes. Experimental results on standard binary datasets show that the algorithm has good generalization capability, comparable to linear SVMs. We also present experimental results on extensions of linear SVMPT to multiclass datasets and datasets with fuzzy membership for each datapoint. © 2010 IEEE.

DSDE 2010 - International Conference on Data Storage and Data Engineering

Binary classification using linear SVM pyramidal tree

We present an improved version of One-Against-All (OAA) method for multiclass SVM classification based on a decision tree approach. The proposed decision tree based OAA (DT-OAA) is aimed at increasing the classification speed of OAA by using posterior probability estimates of binary SVM outputs. DT-OAA decreases the average number of binary SVM tests required in testing phase to a greater extent when compared to OAA and other multiclass SVM methods. For a balanced multiclass dataset with K classes, under best situation, DT-OAA requires only (K + 1)/2 binary tests on an average as opposed to K binary tests in OAA; however, on imbalanced multiclass datasets we observed DT-OAA to be much faster with proper selection of order in which the binary SVMs are arranged in the decision tree. Computational comparisons on publicly available datasets indicate that the proposed method can achieve almost the same classification accuracy as that of OAA, but is much faster in decision making. © 2010 Springer Science+Business Media, LLC.

Neural Processing Letters

Fast multiclass SVM classification using decision tree based one-against-all method

Two-player zero-sum Markov game framework offers an effective platform for designing robust controllers. In the Markov game-based learning, theoretical convergence of the learning process with the function approximator cannot be guaranteed. However, fusing Q-learning with decision tree (DT) function approximator has shown good learning performance and more reliable convergence. It scales better to larger input spaces with lower memory requirements, and can solve problems that are infeasible using table lookup. This motivates us to introduce DT function approximator in Markov game reinforcement learning (RL) framework. This approach works, though it deals with only discrete actions. In realistic applications, it is imperative to deal with continuous state-action spaces. In this paper, we propose Markov game framework for continuous state-action space systems using fuzzy DT as a function approximator. Simulation experiments on a two-link robot manipulator bring out the importance of the proposed structure in terms of better robust performance and computational efficiency. Copyright © 2010 Inderscience Enterprises Ltd.

International Journal of Automation and Control

A fuzzy decision tree-based robust Markov game controller for robot manipulators

The paper presents a novel framework for learning the hash functions for indexing through Multiple Kernel Learning. The Distance Based Hashing function is applied which does the object projection to hash space by preserving inter object distances. In recent works, the kernel matrix has been proved to be more accurate representation of similarity in various recognition problems. Our framework learns the optimal kernel for hashing by parametrized linear combination of base kernels. A novel application of Genetic Algorithm for the optimization of kernel combination parameters is presented. We also define new texture based feature representation for images. Our proposed framework can also be applied for optimal combination of multiple sources for indexing. The evaluation of the proposed framework is presented for CIFAR-10 dataset 1 by applying individual and combination of different features. Additionally, the primary experimental results with MNIST dataset2 is also presented. © 2010 ACM.

Multiple kernel learning for image indexing

We propose knowledge based versions of a relatively new family of SVM algorithms based on two non-parallel hyperplanes. Specifically, we consider prior knowledge in the form of multiple polyhedral sets and incorporate the same into the formulation of linear Twin SVM (TWSVM)/Least Squares Twin SVM (LSTWSVM) and term them as knowledge based TWSVM (KBTWSVM)/knowledge based LSTWSVM (KBLSTWSVM). Both of these formulations are capable of generating non-parallel hyperplanes based on real-world data and prior knowledge. We derive the solution of KBLSTWSVM and use it in our computational experiments for comparison against other linear knowledge based SVM formulations. Our experiments show that KBLSTWSVM is a versatile classifier whose solution is extremely simple when compared with other linear knowledge based SVM algorithms. © 2010 Elsevier Inc. All rights reserved.

Information Sciences

Knowledge based Least Squares Twin support vector machines

Reinforcement learning (RL) has now evolved as a major technique for adaptive optimal control of nonlinear systems. However, majority of the RL algorithms proposed so far impose a strong constraint on the structure of environment dynamics by assuming that it operates as a Markov decision process (MDP). An MDP framework envisages a single agent operating in a stationary environment thereby limiting the scope of application of RL to control problems. Recently, a new direction of research has focused on proposing Markov games as an alternative system model to enhance the generality and robustness of the RL based approaches. This paper aims to present this new direction that seeks to synergize broad areas of RL and Game theory, as an interesting and challenging avenue for designing intelligent and reliable controllers. First, we briefly review some representative RL algorithms for the sake of completeness and then describe the recent direction that seeks to integrate RL and game theory. Finally, open issues are identified and future research directions outlined. © 2009 Elsevier B.V. All rights reserved.

Applied Soft Computing Journal

Synergizing reinforcement learning and game theory-A new direction for control

The paper presents application of multiple features for word based document image indexing and retrieval. A novel framework to perform Multiple Kernel Learning for indexing using the Kernel based Distance Based Hashing is proposed. The Genetic Algorithm based framework is used for optimization. Two different features representing the structural organization of word shape are defined. The optimal combination of both the features for indexing is learned by performing MKL. The retrieval results for document collection belonging to Devanagari script are presented. © 2010 IEEE.

Proceedings - International Conference on Pattern Recognition

Document image retrieval using feature combination in Kernel space

For sequential design processes, the min-max strategy minimizes the worst-case performance cost. This is a game against nature, where the agent attempts to minimize a specified cost criterion, while nature attempts to maximize it. In this paper, we formulate the problem of decision making under uncertainty as a game in which the opponent (nature) is " disinterested" and plays at random, while the agent forces to pick a strategy that maximizes the probability of wining. The potency of proposed worst-case design strategy for games against nature has been established through simulation experiments on inverted-pendulum swing-up. Simulation results show the accelerated learning, and better relative stability of the system. ©2010 IEEE.

Proceedings - 2010 IEEE International Conference on Intelligent Computing and Intelligent Systems, ICIS 2010

Markov game based control: Worst case design strategies for games against nature

The paper presents three novel features for handwritten data based identity recognition. A novel framework for combining the features for identification is presented. The framework combines the features in kernel space in MKL based framework. The application of features individually and in combination is presented for writer recognition and signature verification. The writer recognition results have been presented for Devanagari script input and signature verification results have been presented for open dataset [1]. The experiments have shown encouraging results. © 2010 IEEE.

Proceedings - 12th International Conference on Frontiers in Handwriting Recognition, ICFHR 2010

Identity determination with offline handwritten input using multi kernel feature combination

The present work is part of ongoing effort to improve the performance of Gujarati character recognition. In the recent advancement in kernel methods, the novel concept of multiple kernel learning(MKL) has given improved results for many problems. In this paper, we present novel application ofMKL for Gujarati character recognition. We have applied three different feature representations for symbols obtained after zone wise segmentation of Gujarati text. TheMKLbased classification is proposed, where the MKL is used for learning optimal combination of different features for classification. In addition MKL based classification results for different features is also presented. The multiclass classification is performed in Decision DAG framework. The comparison results in 1-Vs-1 framework and using KNN classifier is also presented. The experiments have shown substantial improvement in earlier results. © Copyright 2010 ACM.

Use of MKL as symbol classifier for Gujarati character recognition

Multiclass support vector machine (SVM) methods are well studied in recent literature. Comparison studies on UCI/statlog multiclass datasets suggest using one-against-one method for multiclass SVM classification. However, in unilabel (multiclass) text categorization with SVMs, no comparison studies exist with one-against-one and other methods, e.g. one-against-all and several well-known improvements to these approaches. In this paper, we bridge this gap by performing empirical comparison of standard one-against-all and one-against-one, together with three improvements to these standard approaches for unilabel text categorization with SVM as base binary learner. We performed all our experiments on three standard text corpuses using two types of document representation. Outcome of our experiments partly support Rifkin and Klautau's (2004) statement that, for small scale unilabel text categorization tasks, if parameters of the classifiers are well tuned, one-against-all will have better performance than one-against-one and other methods. © 2010 Elsevier B.V. All rights reserved.