Big data is a term for data sets that are so large or complex that traditional data processing applications are inadequate. Challenges include analysis, data curtain, search, sharing, storage, transfer, visualization, querying & information privacy. term often refers simply to use of predictive analytics or certain other advanced methods to extract value from data, & seldom to a particular size...

Statistical Relational Learning and Probabilistic Inductive Logic Programming are two emerging fields that use representation languages able to combine logic and probability. In the field of Logic Programming, the distribution semantics is one of the prominent approaches for representing uncertainty and underlies many languages such as ICL, PRISM, ProbLog and LPADs. Learning the parameters for ...

Object detection when provided image-level labels instead of instance-level labels (i.e., bounding boxes) during training is an important problem in computer vision, since large scale image datasets with instance-level labels are extremely costly to obtain. In this paper, we address this challenging problem by developing an ExpectationMaximization (EM) based object detection method using deep c...

Expectation Maximization (EM) is an efficient mixture-model based clustering method. In this paper, authors made an attempt to scale-up the algorithm, by reducing the computation time required for computing quadratic term, without sacrificing the accuracy. Probability density function (pdf) is to be calculated in EM, which involves evaluating quadratic term calculation. Three recursive approach...

We introduce a new class of “maximization expectation” (ME) algorithms where we maximize over hidden variables but marginalize over random parameters. This reverses the roles of expectation and maximization in the classical EM algorithm. In the context of clustering, we argue that these hard assignments open the door to very fast implementations based on data-structures such as kdtrees and cong...

The statistical theory of shape plays a prominent role in applications such as object recognition and medical imaging. An important parameterized family of probability densities defined on the locations of landmark-points is given by the offset-normal shape distributions introduced in [7]. In this paper we present an EM algorithm for learning the parameters of the offset-normal shape distributi...

Iterative clustering algorithms commonly do not lead to optimal cluster solutions. Partitions that are generated by these algorithms are known to be sensitive to the initial partitions that are fed as an input parameter. A “good” selection of initial partitions is an essential clustering problem. In this paper we introduce a new method for constructing the initial partitions set to be used by t...

The paper proposes a new edge-based multi-object tracking framework, MOTEXATION, which deals with tracking multiple objects with occlusions using the Expectation-Maximization (EM) algorithm and a novel edge-based appearance model. In the edge-based appearance model, an object is modelled by a mixture of a non-parametric contour model and a non-parametric edge model using kernel density estimati...

The Baum-Welch algorithm is a technique for the maximum likelihood parameter estimation of probabilistic functions of Markov processes. We apply this technique to nonstationary Markov processes and explore a relationship between the Baum-Welch algorithm and the BCJR algorithm. Furthermore, we apply the Baum-Welch algorithm to two nonstationary Markov processes and obtain the turbo decoding algo...