Today most of the information in all areas is available over the web. It increases the web utilization as well as attracts the interest of researchers to improve the effectiveness of web access and web utilization. As the number of web clients gets increased, the bandwidth sharing is performed that decreases the web access efficiency. Web page prefetching improves the effectiveness of web access by availing the next required web page before the user demand. It is an intelligent predictive mining that analyze the user web access history and predict the next page. In this work, vague improved markov model is presented to perform the prediction. In this work, vague rules are suggested to perform the pruning at different levels of markov model. Once the prediction table is generated, the association mining will be implemented to identify the most effective next page. In this paper, an integrated model is suggested to improve the prediction accuracy and effectiveness.

The increasing demand of world wide web raises the need of predicting the user's web page request.The most widely used approach to predict the web pages is the pattern discovery process of Web usage mining. This process involves inevitability of many techniques like Markov model, association rules and clustering. Fuzzy theory with different techniques has been introduced for the better results. Our focus is on Markov models. This paper is introducing the vague Rules with Markov models for more accuracy using the vague set theory.

Endowing the semantically-oblivious Internet with Intelligence would advance the Internet capability to learn traffic behavior and to predict future events. In this paper, we propose a hybrid intelligence memory system, or NetMem, for network-semantics reasoning and targeting Internet intelligence. NetMem provides a memory structure, mimicking the human memory functionalities, via short-term memory (StM) and long-term memory (LtM). NetMem has the capability to build runtime accessible dynamic network-concept ontology (DNCO) at different levels of granularity. We integrate Latent Dirichlet Allocation (LDA) and Hidden Markov Models (HMM) to extract network-semantics based on learning patterns and recognizing features with syntax and semantic dependencies. Due to the large scale and high-dimensionality of Internet data, we utilize the Locality Sensitive Hashing (LSH) algorithm for data dimensionality reduction. Simulation results using real network traffic show that NetMem with hybrid intelligence learn traffic data semantics effectively and efficiently even with significant reduction in volume and dimensionality of data, thus enhancing Internet intelligence for self-/situation-awareness and event/behavior prediction.

Robots may encounter undesirable outcomes due to failures during the execution of their plans in the physical world. Failures should be detected, and the underlying reasons should be found by the robot in order to handle these failure situations efficiently. Sometimes, there may be more than one cause of a failure, and they are not necessarily related to the action in execution. In this paper, we propose a temporal and Hierarchical Hidden Markov Model (HHMM) based failure isolation method. These HHMMs run in parallel to determine causes of unexpected deviations. Experiments on our Pioneer 3-AT robot show that our method successfully isolates failures suggesting possible causes.

A new approach to maximum likelihood learning of discrete graphical models and RBM in particular is introduced. Our method, Perturb and Descend (PD) is inspired by two ideas (I) perturb and MAP method for sampling (II) learning by Contrastive Divergence minimization. In contrast to perturb and MAP, PD leverages training data to learn the models that do not allow efficient MAP estimation. During the learning, to produce a sample from the current model, we start from a training data and descend in the energy landscape of the "perturbed model", for a fixed number of steps, or until a local optima is reached. For RBM, this involves linear calculations and thresholding which can be very fast. Furthermore we show that the amount of perturbation is closely related to the temperature parameter and it can regularize the model by producing robust features resulting in sparse hidden layer activation.

We present a machine learning framework for modeling protein dynamics. Our approach uses L1-regularized, reversible hidden Markov models to understand large protein datasets generated via molecular dynamics simulations. Our model is motivated by three design principles: (1) the requirement of massive scalability; (2) the need to adhere to relevant physical law; and (3) the necessity of providing accessible interpretations, critical for both cellular biology and rational drug design. We present an EM algorithm for learning and introduce a model selection criteria based on the physical notion of convergence in relaxation timescales. We contrast our model with standard methods in biophysics and demonstrate improved robustness. We implement our algorithm on GPUs and apply the method to two large protein simulation datasets generated respectively on the NCSA Bluewaters supercomputer and the Folding@Home distributed computing network. Our analysis identifies the conformational dynamics of the ubiquitin protein critical to cellular signaling, and elucidates the stepwise activation mechanism of the c-Src kinase protein.

A sequence of random variables is exchangeable if its joint distribution is invariant under variable permutations. We introduce exchangeable variable models (EVMs) as a novel class of probabilistic models whose basic building blocks are partially exchangeable sequences, a generalization of exchangeable sequences. We prove that a family of tractable EVMs is optimal under zero-one loss for a large class of functions, including parity and threshold functions, and strictly subsumes existing tractable independence-based model families. Extensive experiments show that EVMs outperform state of the art classifiers such as SVMs and probabilistic models which are solely based on independence assumptions.

This paper introduces a novel mixture model-based approach for simultaneous clustering and optimal segmentation of functional data which are curves presenting regime changes. The proposed model consists in a finite mixture of piecewise polynomial regression models. Each piecewise polynomial regression model is associated with a cluster, and within each cluster, each piecewise polynomial component is associated with a regime (i.e., a segment). We derive two approaches for learning the model parameters. The former is an estimation approach and consists in maximizing the observed-data likelihood via a dedicated expectation-maximization (EM) algorithm. A fuzzy partition of the curves in K clusters is then obtained at convergence by maximizing the posterior cluster probabilities. The latter however is a classification approach and optimizes a specific classification likelihood criterion through a dedicated classification expectation-maximization (CEM) algorithm. The optimal curve segmentation is performed by using dynamic programming. In the classification approach, both the curve clustering and the optimal segmentation are performed simultaneously as the CEM learning proceeds. We show that the classification approach is the probabilistic version that generalizes the deterministic K-means-like algorithm proposed in H\'ebrail et al. (2010). The proposed approach is evaluated using simulated curves and real-world curves. Comparisons with alternatives including regression mixture models and the K-means like algorithm for piecewise regression demonstrate the effectiveness of the proposed approach.

This paper presents a reinforcement learning algorithm for solving infinite horizon Markov Decision Processes under the expected total discounted reward criterion when both the state and action spaces are continuous. This algorithm is based on Watkins' Q-learning, but uses Nadaraya-Watson kernel smoothing to generalize knowledge to unvisited states. As expected, continuity conditions must be imposed on the mean rewards and transition probabilities. Using results from kernel regression theory, this algorithm is proven capable of producing a Q-value function estimate that is uniformly within an arbitrary tolerance of the true Q-value function with probability one. The algorithm is then applied to an example problem to empirically show convergence as well.

Consider a random vector with finite second moments. If its precision matrix is an M-matrix, then all partial correlations are non-negative. If that random vector is additionally Gaussian, the corresponding Markov random field (GMRF) is called attractive. We study estimation of M-matrices taking the role of inverse second moment or precision matrices using sign-constrained log-determinant divergence minimization. We also treat the high-dimensional case with the number of variables exceeding the sample size. The additional sign-constraints turn out to greatly simplify the estimation problem: we provide evidence that explicit regularization is no longer required. To solve the resulting convex optimization problem, we propose an algorithm based on block coordinate descent, in which each sub-problem can be recast as non-negative least squares problem. Illustrations on both simulated and real world data are provided.