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Abstract: Most cognitive architectures rely on discrete representation, both in space (e.g., objects) and in time (e.g., events). However, a robot interaction with the world is inherently continuous, both in space and in time. The segmentation of the stream of perceptual inputs a robot receives into discrete and meanin... |
Title: Statistical Inference in Dynamic Treatment Regimes |
Abstract: Dynamic treatment regimes are of growing interest across the clinical sciences as these regimes provide one way to operationalize and thus inform sequential personalized clinical decision making. A dynamic treatment regime is a sequence of decision rules, with a decision rule per stage of clinical interventio... |
Title: RoboCast: Asynchronous Communication in Robot Networks |
Abstract: This paper introduces the communication abstraction. The RoboCast allows a swarm of non oblivious, anonymous robots that are only endowed with visibility sensors and do not share a common coordinate system, to asynchronously exchange information. We propose a generic framework that covers a large class of asy... |
Title: Testing SDRT's Right Frontier |
Abstract: The Right Frontier Constraint (RFC), as a constraint on the attachment of new constituents to an existing discourse structure, has important implications for the interpretation of anaphoric elements in discourse and for Machine Learning (ML) approaches to learning discourse structures. In this paper we provid... |
Title: Counterexample Guided Abstraction Refinement Algorithm for Propositional Circumscription |
Abstract: Circumscription is a representative example of a nonmonotonic reasoning inference technique. Circumscription has often been studied for first order theories, but its propositional version has also been the subject of extensive research, having been shown equivalent to extended closed world assumption (ECWA). ... |
Title: Performance Comparison of SVM and ANN for Handwritten Devnagari Character Recognition |
Abstract: Classification methods based on learning from examples have been widely applied to character recognition from the 1990s and have brought forth significant improvements of recognition accuracies. This class of methods includes statistical methods, artificial neural networks, support vector machines (SVM), mult... |
Title: Recognition of Non-Compound Handwritten Devnagari Characters using a Combination of MLP and Minimum Edit Distance |
Abstract: This paper deals with a new method for recognition of offline Handwritten non-compound Devnagari Characters in two stages. It uses two well known and established pattern recognition techniques: one using neural networks and the other one using minimum edit distance. Each of these techniques is applied on diff... |
Title: Application of Statistical Features in Handwritten Devnagari Character Recognition |
Abstract: In this paper a scheme for offline Handwritten Devnagari Character Recognition is proposed, which uses different feature extraction methodologies and recognition algorithms. The proposed system assumes no constraints in writing style or size. First the character is preprocessed and features namely : Chain cod... |
Title: Multiple Classifier Combination for Off-line Handwritten Devnagari Character Recognition |
Abstract: This work presents the application of weighted majority voting technique for combination of classification decision obtained from three Multi_Layer Perceptron(MLP) based classifiers for Recognition of Handwritten Devnagari characters using three different feature sets. The features used are intersection, shad... |
Title: A Two Stage Classification Approach for Handwritten Devanagari Characters |
Abstract: The paper presents a two stage classification approach for handwritten devanagari characters The first stage is using structural properties like shirorekha, spine in character and second stage exploits some intersection features of characters which are fed to a feedforward neural network. Simple histogram bas... |
Title: A novel approach for handwritten Devnagari character recognition |
Abstract: In this paper a method for recognition of handwritten devanagari characters is described. Here, feature vector is constituted by accumulated directional gradient changes in different segments, number of intersections points for the character, type of spine present and type of shirorekha present in the charact... |
Title: Classification Of Gradient Change Features Using MLP For Handwritten Character Recognition |
Abstract: A novel, generic scheme for off-line handwritten English alphabets character images is proposed. The advantage of the technique is that it can be applied in a generic manner to different applications and is expected to perform better in uncertain and noisy environments. The recognition scheme is using a multi... |
Title: FPGA Based Assembling of Facial Components for Human Face Construction |
Abstract: This paper aims at VLSI realization for generation of a new face from textual description. The FASY (FAce SYnthesis) System is a Face Database Retrieval and new Face generation System that is under development. One of its main features is the generation of the requested face when it is not found in the existi... |
Title: Fuzzy Classification of Facial Component Parameters |
Abstract: This paper presents a novel type-2 Fuzzy logic System to define the Shape of a facial component with the crisp output. This work is the part of our main research effort to design a system (called FASY) which offers a novel face construction approach based on the textual description and also extracts and analy... |
Title: Survey of Nearest Neighbor Techniques |
Abstract: The nearest neighbor (NN) technique is very simple, highly efficient and effective in the field of pattern recognition, text categorization, object recognition etc. Its simplicity is its main advantage, but the disadvantages can't be ignored even. The memory requirement and computation complexity also matter.... |
Title: The Computational Complexity of Estimating Convergence Time |
Abstract: An important problem in the implementation of Markov Chain Monte Carlo algorithms is to determine the convergence time, or the number of iterations before the chain is close to stationarity. For many Markov chains used in practice this time is not known. Even in cases where the convergence time is known to be... |
Title: Uncertainty of visual measurement and efficient allocation of sensory resources |
Abstract: We review the reasoning underlying two approaches to combination of sensory uncertainties. First approach is noncommittal, making no assumptions about properties of uncertainty or parameters of stimulation. Then we explain the relationship between this approach and the one commonly used in modeling "higher le... |
Title: A new lifetime model with decreasing failure rate |
Abstract: In this paper we introduce a new lifetime distribution by compounding exponential and Poisson-Lindley distributions, named exponential Poisson-Lindley distribution. Several properties are derived, such as density, failure rate, mean lifetime, moments, order statistics and R\'enyi entropy. Furthermore, estimat... |
Title: A Bayesian View of the Poisson-Dirichlet Process |
Abstract: The two parameter Poisson-Dirichlet Process (PDP), a generalisation of the Dirichlet Process, is increasingly being used for probabilistic modelling in discrete areas such as language technology, bioinformatics, and image analysis. There is a rich literature about the PDP and its derivative distributions such... |
Title: Repairing People Trajectories Based on Point Clustering |
Abstract: This paper presents a method for improving any object tracking algorithm based on machine learning. During the training phase, important trajectory features are extracted which are then used to calculate a confidence value of trajectory. The positions at which objects are usually lost and found are clustered ... |
Title: Transfer Entropy on Rank Vectors |
Abstract: Transfer entropy (TE) is a popular measure of information flow found to perform consistently well in different settings. Symbolic transfer entropy (STE) is defined similarly to TE but on the ranks of the components of the reconstructed vectors rather than the reconstructed vectors themselves. First, we correc... |
Title: The Transfer of Evolved Artificial Immune System Behaviours between Small and Large Scale Robotic Platforms |
Abstract: This paper demonstrates that a set of behaviours evolved in simulation on a miniature robot (epuck) can be transferred to a much larger scale platform (a virtual Pioneer P3-DX) that also differs in shape, sensor type, sensor configuration and programming interface. The chosen architecture uses a reinforcement... |
Title: Additive Non-negative Matrix Factorization for Missing Data |
Abstract: Non-negative matrix factorization (NMF) has previously been shown to be a useful decomposition for multivariate data. We interpret the factorization in a new way and use it to generate missing attributes from test data. We provide a joint optimization scheme for the missing attributes as well as the NMF facto... |
Title: Non-uniform state space reconstruction and coupling detection |
Abstract: We investigate the state space reconstruction from multiple time series derived from continuous and discrete systems and propose a method for building embedding vectors progressively using information measure criteria regarding past, current and future states. The embedding scheme can be adapted for different... |
Title: Improving Iris Recognition Accuracy By Score Based Fusion Method |
Abstract: Iris recognition technology, used to identify individuals by photographing the iris of their eye, has become popular in security applications because of its ease of use, accuracy, and safety in controlling access to high-security areas. Fusion of multiple algorithms for biometric verification performance impr... |
Title: IMP: A Message-Passing Algorithmfor Matrix Completion |
Abstract: A new message-passing (MP) method is considered for the matrix completion problem associated with recommender systems. We attack the problem using a (generative) factor graph model that is related to a probabilistic low-rank matrix factorization. Based on the model, we propose a new algorithm, termed IMP, for... |
Title: Query Strategies for Evading Convex-Inducing Classifiers |
Abstract: Classifiers are often used to detect miscreant activities. We study how an adversary can systematically query a classifier to elicit information that allows the adversary to evade detection while incurring a near-minimal cost of modifying their intended malfeasance. We generalize the theory of Lowd and Meek (... |
Title: Discovering Graphical Granger Causality Using the Truncating Lasso Penalty |
Abstract: Components of biological systems interact with each other in order to carry out vital cell functions. Such information can be used to improve estimation and inference, and to obtain better insights into the underlying cellular mechanisms. Discovering regulatory interactions among genes is therefore an importa... |
Title: Computational Model of Music Sight Reading: A Reinforcement Learning Approach |
Abstract: Although the Music Sight Reading process has been studied from the cognitive psychology view points, but the computational learning methods like the Reinforcement Learning have not yet been used to modeling of such processes. In this paper, with regards to essential properties of our specific problem, we cons... |
Title: A Fast Decision Technique for Hierarchical Hough Transform for Line Detection |
Abstract: Many techniques have been proposed to speedup the performance of classic Hough Transform. These techniques are primarily based on converting the voting procedure to a hierarchy based voting method. These methods use approximate decision-making process. In this paper, we propose a fast decision making process ... |
Title: A Reinforcement Learning Model Using Neural Networks for Music Sight Reading Learning Problem |
Abstract: Music Sight Reading is a complex process in which when it is occurred in the brain some learning attributes would be emerged. Besides giving a model based on actor-critic method in the Reinforcement Learning, the agent is considered to have a neural network structure. We studied on where the sight reading pro... |
Title: Minimax Manifold Estimation |
Abstract: We find the minimax rate of convergence in Hausdorff distance for estimating a manifold M of dimension d embedded in R^D given a noisy sample from the manifold. We assume that the manifold satisfies a smoothness condition and that the noise distribution has compact support. We show that the optimal rate of co... |
Title: Graphical Models as Block-Tree Graphs |
Abstract: We introduce block-tree graphs as a framework for deriving efficient algorithms on graphical models. We define block-tree graphs as a tree-structured graph where each node is a cluster of nodes such that the clusters in the graph are disjoint. This differs from junction-trees, where two clusters connected by ... |
Title: Quickest Detection with Social Learning: Interaction of local and global decision makers |
Abstract: We consider how local and global decision policies interact in stopping time problems such as quickest time change detection. Individual agents make myopic local decisions via social learning, that is, each agent records a private observation of a noisy underlying state process, selfishly optimizes its local ... |
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