Abstract
Nonnegative matrix factorization (nmf) is a powerful and well-established technique in data mining. We generalize nmf signi_cantly by introducing several new and rich formulations for sparse nonnegative matrix factorization (snmf). Our formulations are substantially more general than previously studied snmf problems. In particular, we model sparsity via constraints and regularizers, for which we describe a variety of concrete choices of sparsity tuning functions. To solve snmf, we develop convergent and computationally e_cient ....etc

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Abstract:
A seismic loss curve of a real-world building is obtained by means of state-of-the-art models for the impending ground motion, the structure, the damage of structural and nonstructural components, and the ensuing losses. In particular, the seismic hazard at the building s location _Vancouver, Canada_ is described by a comprehensive probabilistic model. It is argued that this ground motion model is particularly appropriate in reliability analysis compared with the more common utilization of a limited set of scaled ground motions. I ....etc

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To get full information or details of visual cryptography scheme with dynamic group please have a look on the pages

http://seminarsprojects.net/Thread-visual-cryptography-documentation-pdf-file

http://seminarsprojects.net/Thread-visual-cryptography--26143

http://seminarsprojects.net/Thread-embedded-extended-visual-cryptography-schemes

if you again feel trouble on visual cryptography scheme with dynamic group please reply in that page and ask specific fields in visual cryptography scheme with dynamic group ....etc

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Icici bank learning matrix all topics answer key ....etc

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Approach based on homology and composition-based approach to further study the functional nucleus (ie, the microbial nucleus and nucleus of the gene, correspondingly). In the proposed method, the identification of the main groups of functionality is achieved by generative theme modelling, which is able to extract useful information from unlabelled data. We first demonstrate that generative topic model can be used to model the taxon abundance information obtained by homology-based approach and study the microbial nucleus. The model considers eac ....etc

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Near-Optimal Multiuser Detection in Synchronous CDMA Using Probabilistic Data Association
Presented By:
J. Luo, Student Member, IEE, K. R. Pattipati, Fellow, IEE, P. K. Willett, Senior Member, IEE, and F. Hasegawa, Member, IEE
Abstract
A Probabilistic Data Association (PDA) method is proposed in this letter for multiuser detection over synchronous code-division multiple-access (CDMA) communication channels. PDA models the undecided user signals as binary random variables. By approximating the inter-user interference (IU ....etc

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To get full information or details of a probabilistic model of visual cryptography scheme with dynamic group with disadvantages please have a look on the pages

http://seminarsprojects.net/Thread-visual-cryptography-documentation-pdf-file

http://seminarsprojects.net/Thread-visual-cryptography--26143?pid=54598

http://seminarsprojects.net/Thread-visual-cryptography--26143

http://seminarsprojects.net/Thread-visual-cryptographic-steganography-in-images

http://seminarsprojects.net/Thread-visual-cryptography

if you again feel trouble on a probab ....etc

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There has been a lot of research in the field of definition of the representation, the data fusion, and the generation of probabilistic occupancy maps. Here the occupancy maps are processed as textured images to extract meaningful information which is useful for the robot navigation. modern segmentation techniques' application is considered over the probabilistic occupancy maps. The local binary pattern and contrast is used as a basis for enhancements. The enhanced LBP/C segmentation technique handles occupancy uncertainty. free, unknown, and o ....etc

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hi plz send detail riport for the below title.

Topology preserving non negative matrix factorization for face recognition ....etc

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Unsupervised Learning of Probabilistic Object Models


INTRODUCTION
RECENT work on object classification and recognition has
tended to represent objects in terms of spatial configurations
of features at a small number of interest points ,
, , , , . Such models are computationally efficient,
for both learning and inference, and can be very effective for
tasks such as classification. But they have two major
disadvantages: 1) the sparseness of their representations
re ....etc

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