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Title: A pruned dynamic programming algorithm to recover the best segmentations with $1$ to $K_max$ change-points |
Abstract: A common computational problem in multiple change-point models is to recover the segmentations with $1$ to $K_max$ change-points of minimal cost with respect to some loss function. Here we present an algorithm to prune the set of candidate change-points which is based on a functional representation of the cos... |
Title: A New Generalized Kumaraswamy Distribution |
Abstract: A new five-parameter continuous distribution which generalizes the Kumaraswamy and the beta distributions as well as some other well-known distributions is proposed and studied. The model has as special cases new four- and three-parameter distributions on the standard unit interval. Moments, mean deviations, ... |
Title: Message-Passing Inference on a Factor Graph for Collaborative Filtering |
Abstract: This paper introduces a novel message-passing (MP) framework for the collaborative filtering (CF) problem associated with recommender systems. We model the movie-rating prediction problem popularized by the Netflix Prize, using a probabilistic factor graph model and study the model by deriving generalization ... |
Title: On Tsallis Entropy Bias and Generalized Maximum Entropy Models |
Abstract: In density estimation task, maximum entropy model (Maxent) can effectively use reliable prior information via certain constraints, i.e., linear constraints without empirical parameters. However, reliable prior information is often insufficient, and the selection of uncertain constraints becomes necessary but ... |
Title: Constructing Summary Statistics for Approximate Bayesian Computation: Semi-automatic ABC |
Abstract: Many modern statistical applications involve inference for complex stochastic models, where it is easy to simulate from the models, but impossible to calculate likelihoods. Approximate Bayesian computation (ABC) is a method of inference for such models. It replaces calculation of the likelihood by a step whic... |
Title: Regularized Richardson-Lucy Algorithm for Sparse Reconstruction of Poissonian Images |
Abstract: Restoration of digital images from their degraded measurements has always been a problem of great theoretical and practical importance in numerous applications of imaging sciences. A specific solution to the problem of image restoration is generally determined by the nature of degradation phenomenon as well a... |
Title: Signature Recognition using Multi Scale Fourier Descriptor And Wavelet Transform |
Abstract: This paper present a novel off-line signature recognition method based on multi scale Fourier Descriptor and wavelet transform . The main steps of constructing a signature recognition system are discussed and experiments on real data sets show that the average error rate can reach 1%. Finally we compare 8 dis... |
Title: Ontology-supported processing of clinical text using medical knowledge integration for multi-label classification of diagnosis coding |
Abstract: This paper discusses the knowledge integration of clinical information extracted from distributed medical ontology in order to ameliorate a machine learning-based multi-label coding assignment system. The proposed approach is implemented using a decision tree based cascade hierarchical technique on the univer... |
Title: Algebraic Comparison of Partial Lists in Bioinformatics |
Abstract: The outcome of a functional genomics pipeline is usually a partial list of genomic features, ranked by their relevance in modelling biological phenotype in terms of a classification or regression model. Due to resampling protocols or just within a meta-analysis comparison, instead of one list it is often the ... |
Title: Importance of Sources using the Repeated Fusion Method and the Proportional Conflict Redistribution Rules #5 and #6 |
Abstract: We present in this paper some examples of how to compute by hand the PCR5 fusion rule for three sources, so the reader will better understand its mechanism. We also take into consideration the importance of sources, which is different from the classical discounting of sources. |
Title: Belief Propagation for Min-cost Network Flow: Convergence and Correctness |
Abstract: Message passing type algorithms such as the so-called Belief Propagation algorithm have recently gained a lot of attention in the statistics, signal processing and machine learning communities as attractive algorithms for solving a variety of optimization and inference problems. As a decentralized, easy to im... |
Title: Fuzzy Logic of Speed and Steering Control System for Three Dimensional Line Following of an Autonomous Vehicle |
Abstract: ... This paper is to describe exploratory research on the design of a modular autonomous mobile robot controller. The controller incorporates a fuzzy logic [8] [9] approach for steering and speed control [37], a FL approach for ultrasound sensing and an overall expert system for guidance. The advantages of a ... |
Title: A Robust Fuzzy Clustering Technique with Spatial Neighborhood Information for Effective Medical Image Segmentation |
Abstract: Medical image segmentation demands an efficient and robust segmentation algorithm against noise. The conventional fuzzy c-means algorithm is an efficient clustering algorithm that is used in medical image segmentation. But FCM is highly vulnerable to noise since it uses only intensity values for clustering th... |
Title: New Clustering Algorithm for Vector Quantization using Rotation of Error Vector |
Abstract: The paper presents new clustering algorithm. The proposed algorithm gives less distortion as compared to well known Linde Buzo Gray (LBG) algorithm and Kekre's Proportionate Error (KPE) Algorithm. Constant error is added every time to split the clusters in LBG, resulting in formation of cluster in one directi... |
Title: On the bias of BFS |
Abstract: Breadth First Search (BFS) and other graph traversal techniques are widely used for measuring large unknown graphs, such as online social networks. It has been empirically observed that an incomplete BFS is biased toward high degree nodes. In contrast to more studied sampling techniques, such as random walks,... |
Title: An Analytical Study on Behavior of Clusters Using K Means, EM and K* Means Algorithm |
Abstract: Clustering is an unsupervised learning method that constitutes a cornerstone of an intelligent data analysis process. It is used for the exploration of inter-relationships among a collection of patterns, by organizing them into homogeneous clusters. Clustering has been dynamically applied to a variety of task... |
Title: A New Approach to Lung Image Segmentation using Fuzzy Possibilistic C-Means Algorithm |
Abstract: Image segmentation is a vital part of image processing. Segmentation has its application widespread in the field of medical images in order to diagnose curious diseases. The same medical images can be segmented manually. But the accuracy of image segmentation using the segmentation algorithms is more when com... |
Title: Terrorism Event Classification Using Fuzzy Inference Systems |
Abstract: Terrorism has led to many problems in Thai societies, not only property damage but also civilian casualties. Predicting terrorism activities in advance can help prepare and manage risk from sabotage by these activities. This paper proposes a framework focusing on event classification in terrorism domain using... |
Title: SAR Image Segmentation using Vector Quantization Technique on Entropy Images |
Abstract: The development and application of various remote sensing platforms result in the production of huge amounts of satellite image data. Therefore, there is an increasing need for effective querying and browsing in these image databases. In order to take advantage and make good use of satellite images data, we m... |
Title: Probabilistic Semantic Web Mining Using Artificial Neural Analysis |
Abstract: Most of the web user's requirements are search or navigation time and getting correctly matched result. These constrains can be satisfied with some additional modules attached to the existing search engines and web servers. This paper proposes that powerful architecture for search engines with the title of Pr... |
Title: Design and analysis of fractional factorial experiments from the viewpoint of computational algebraic statistics |
Abstract: We give an expository review of applications of computational algebraic statistics to design and analysis of fractional factorial experiments based on our recent works. For the purpose of design, the techniques of Gr\"obner bases and indicator functions allow us to treat fractional factorial designs without d... |
Title: Feature Level Fusion of Face and Palmprint Biometrics by Isomorphic Graph-based Improved K-Medoids Partitioning |
Abstract: This paper presents a feature level fusion approach which uses the improved K-medoids clustering algorithm and isomorphic graph for face and palmprint biometrics. Partitioning around medoids (PAM) algorithm is used to partition the set of n invariant feature points of the face and palmprint images into k clus... |
Title: Maximized Posteriori Attributes Selection from Facial Salient Landmarks for Face Recognition |
Abstract: This paper presents a robust and dynamic face recognition technique based on the extraction and matching of devised probabilistic graphs drawn on SIFT features related to independent face areas. The face matching strategy is based on matching individual salient facial graph characterized by SIFT features as c... |
Title: State-Space Dynamics Distance for Clustering Sequential Data |
Abstract: This paper proposes a novel similarity measure for clustering sequential data. We first construct a common state-space by training a single probabilistic model with all the sequences in order to get a unified representation for the dataset. Then, distances are obtained attending to the transition matrices ind... |
Title: An optimized recursive learning algorithm for three-layer feedforward neural networks for mimo nonlinear system identifications |
Abstract: Back-propagation with gradient method is the most popular learning algorithm for feed-forward neural networks. However, it is critical to determine a proper fixed learning rate for the algorithm. In this paper, an optimized recursive algorithm is presented for online learning based on matrix operation and opt... |
Title: The Socceral Force |
Abstract: We have an audacious dream, we would like to develop a simulation and virtual reality system to support the decision making in European football (soccer). In this review, we summarize the efforts that we have made to fulfil this dream until recently. In addition, an introductory version of FerSML (Footballer ... |
Title: Machine learning approach to inverse problem and unfolding procedure |
Abstract: A procedure for unfolding the true distribution from experimental data is presented. Machine learning methods are applied for simultaneous identification of an apparatus function and solving of an inverse problem. A priori information about the true distribution from theory or previous experiments is used for... |
Title: Matrix Coherence and the Nystrom Method |
Abstract: The Nystrom method is an efficient technique to speed up large-scale learning applications by generating low-rank approximations. Crucial to the performance of this technique is the assumption that a matrix can be well approximated by working exclusively with a subset of its columns. In this work we relate th... |
Title: Dynamic Policy Programming |
Abstract: In this paper, we propose a novel policy iteration method, called dynamic policy programming (DPP), to estimate the optimal policy in the infinite-horizon Markov decision processes. We prove the finite-iteration and asymptotic l\infty-norm performance-loss bounds for DPP in the presence of approximation/estim... |
Title: Estimation for Latent Factor Models for High-Dimensional Time Series |
Abstract: This paper deals with the dimension reduction for high-dimensional time series based on common factors. In particular we allow the dimension of time series $p$ to be as large as, or even larger than, the sample size $n$. The estimation for the factor loading matrix and the factor process itself is carried out... |
Title: An empirical comparative study of approximate methods for binary graphical models; application to the search of associations among causes of death in French death certificates |
Abstract: Looking for associations among multiple variables is a topical issue in statistics due to the increasing amount of data encountered in biology, medicine and many other domains involving statistical applications. Graphical models have recently gained popularity for this purpose in the statistical literature. F... |
Title: Spatio-Temporal Graphical Model Selection |
Abstract: We consider the problem of estimating the topology of spatial interactions in a discrete state, discrete time spatio-temporal graphical model where the interactions affect the temporal evolution of each agent in a network. Among other models, the susceptible, infected, recovered ($SIR$) model for interaction ... |
Title: Asymptotic Equivalence of Bayes Cross Validation and Widely Applicable Information Criterion in Singular Learning Theory |
Abstract: In regular statistical models, the leave-one-out cross-validation is asymptotically equivalent to the Akaike information criterion. However, since many learning machines are singular statistical models, the asymptotic behavior of the cross-validation remains unknown. In previous studies, we established the si... |
Title: Mean field for Markov Decision Processes: from Discrete to Continuous Optimization |
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