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Horse betting statistical approach


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"A Statistical Approach to Betting the Horse Races" provides historical results from over 8000 races and 23 racetracks. In the beginning sections, simple probability and statistics are discussed t...
"A Statistical Approach to Betting the Horse Races" provides historical results from over 8000 races and 23 racetracks. In the beginning sections, simple probability and statistics are discussed t...
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horse betting statistical approach
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horse betting statistical approach We use the pair-copula decomposition of a general multivariate distribution and propose a method for performing inference.
The model construction is hierarchical in nature, the various levels corresponding to the incorporation of more variables in the conditioning sets, using pair-copulae as simple building blocks.
Pair-copula decomposed models also represent a very flexible way to construct higher-dimensional copulae.
We apply the methodology to a financial data set.
Our approach represents the first step towards the development of an unsupervised algorithm that explores the space of possible pair-copula models, that also can be applied to huge data sets automatically.
Using an overcomplete dictionary that contains prototype signal-atoms, signals are described by sparse linear combinations of these atoms.
Applications that use sparse representation are many and include compression, regularization in inverse problems, feature extraction, and more.
Recent activity in this field has concentrated mainly on the study of pursuit algorithms that decompose signals with respect to a given dictionary.
Designing dictionaries to better fit the above model can be done by either selecting one from a prespecified set of linear transforms or adapting the dictionary to a set of training signals.
Both of these techniques have been considered, but this topic is largely still open.
In this paper we propose a novel algorithm for adapting dictionaries in order to achieve sparse signal representations.
Given a set of training signals, we seek the dictionary that leads to the best representation for each member in this set, under strict sparsity constraints.
We present a new method—the K-SVD algorithm—generalizing the K-means clustering process.
K-SVD is an iterative method that alternates between sparse coding of the examples based on the current dictionary and a process of updating the dictionary atoms to better fit the data.
The update of the dictionary columns is combined with an update of the sparse representations, thereby accelerating convergence.
The K-SVD algorithm is flexible and can work with any pursuit method e.
We analyze this algorithm and demonstrate its results both on synthetic tests and in applications on real image data.
Allwein and Robert E.
Harris and Nils L.
Vines generalize the Markov trees often used in modelling highdimensionald istributionsT.
Vines can be used to specify multivariated istributionsin a straightforward way by specifying various marginal distributions and the ways in which these marginals are to be coupled.
Such distributions have applications in uncertainty analysis where the objective is to determine the sensitivity of a model output with respect to the uncertainty in unknown parameters.
Expert information is frequently elicited to determine some quantitative characteristics of the distribution such as rank correlations.
We show that it is simple to constructa minimumi nformationv ine distribution,g iven such expert information.
Sampling from minimum information distributions with given horse betting statistical approach and conditional rank correlations specified on a vine can be performed almost as fast as independent sampling.
A special case of the vine construction generalizes work of Joe and allows the construction of a multivariaten ormald istributionb y specifying a set of partial correlations on which there are no restrictions except the obvious one that a correlation lies between -1 and 1.
COM-34 12pp.
Two feature sets were employed, the first containing perceptual features and MPEG-7 descriptors and the second containing rhythm patterns developed for the SOMeJB project.
The features were measured for 300 sound recordings consisting of 6 different musical instrument classes.
Subsets of the feature set are selected using branch-and-bound search, obtaining the most suitable features for classification.
A class of supervised classifiers is developed based on the non-negative matrix factorization NMF.
The standard NMF method is examined as well as its modifications: the local and the sparse NMF.
The experiments compare the two feature sets alongside the various NMF algorithms.
The results demonstrate an almost perfect classification for the first set using the standard NMF algorithm classification error 1.
Several perceptual features used in sound classification applications as well as MPEG-7 descriptors were measured for 300 sound recordings consisting of 6 different musical instrument classes.
Subsets of the feature set are selected using branch-and-bound search, obtaining the most suitable features for classification, A class of classifiers is developed based on the non-negative matrix factorization NMF.
The standard NMF method is examined as well as its modifications: the local, the sparse, and the discriminant NMF.
The experimental results compare feature subsets of varying sizes alongside the various NMF algorithms.
Berry and Murray Browne and Amy N.
Paul Pauca and Robert J.
Boos and John F.
Series B Methodological Vol.
Rank Analysis of Incomplete Block Designs: I.
Cavanaugh and Robert H.
Clyde and Herbert K.
Cohen and Robert E.
Cohn and Zoubin Ghahramani and Michael I.
Cover and Joy A.
Davidson and Robert J.
Davis and Brian Kulis and Prateek Jain and Suvrit Sra and Inderjit S.
Dumais and George W.
Furnas and Thomas K.
Series B Methodological Vol.
Ding and Xiaofeng He and Hongyuan Zha and Ming Gu and Horst D.
Dobson and Adrian G.
H¥ddotardle and Christian M.
Frey and Relu Patrascu and Tommi S.
Glickman and Shane T.
Series B Methodological Vol.
Hampel and Elvezio M.
Ronchetti and Peter J.
Rousseeuw and Werner A.
Hays and Joseph F.
Hoi and Wei Liu and Michael R.
Although it has successfully been applied in several applications, it does not always result in parts-based representations.
Additionally, we provide complete MATLAB code both for standard NMF and for our extension.
Our hope is that this will further the application of these methods to solving novel data-analysis problems.
Imrey and William D.
Johnson and Gary G.
Jacobs and Michael I.
Jordan and Steven J.
Nowlan and Geoffrey E.
Kieffer and En-hui Yang and Gregory J.
E92-D 3pp.
For example, pixels in digital images and chemical concentrations in bioinformatics are non-negative.
Sparse non-negative matrix factorizations NMFs are useful when the degree of sparseness in the non-negative basis matrix or the non-negative coefficient matrix in an NMF needs to be controlled in approximating high-dimensional data in a lower dimensional space.
Results: In this article, we introduce a novel formulation of sparse NMF and show how the new formulation leads to a convergent sparse NMF algorithm via alternating non-negativity-constrained least squares.
We apply our sparse NMF algorithm to cancer-class discovery and gene expression data analysis and offer biological analysis of the results obtained.
Our experimental results illustrate that the proposed sparse NMF algorithm often achieves better clustering performance with shorter computing time compared to other existing NMF algorithms.
Availability: The software is available as supplementary material.
IIIProceedings of ICSLP 2000, pp.
IT-27 2pp.
Krichevsky and Victor K.
Compound Multinomial Likelihood Functions are Unimodal: Proof of a Conjecture of I.
Simon and John J.
Despite significant research progress in this area, few attempts have been made to establish the connections between various factorization methods while highlighting their differences.
In this paper we aim to provide a comprehensive study on matrix factorization for clustering.
In particular, we present an overview and summary on various matrix factorization algorithms and theoretically analyze the relationships among them.
Experiments are also conducted to empirically evaluate and compare various factorization methods.
In addition, our study also answers several previously unaddressed yet important questions for matrix factorizations including the interpretation and normalization of cluster posterior and the benefits and evaluation of simultaneous clustering.
We expect our study would provide good insights on matrix factorization research for clustering.
Series B Methodological Vol.
Series B Methodological Vol.
Mooney and Robert D.
Nayak приведенная ссылка Horse betting statistical approach N.
Le Méhauté and Q.
Nevil-Manning and Ian H.
Nevill-Manning and Ian H.
Witten and David L.
Sparse coding with an overcomplete basis set: a strategy employed by V1?
Olshausen and David J.
Olshausen and David J.
This paper proposes a new document clustering method using the clustering method based NMF Non-negative Matrix Factorization and refinement of documents in clusters by using coherence of cluster.
The proposed method can improve the quality of document clustering because the re-assigned documents in cluster by using coherence of cluster based similarity between documents, the semantic feature matrix and the semantic variable matrix, which is used in document clustering, can represent an inherent structure of document set better.
The experimental results demonstrate that the proposed method achieves better performance than other document clustering methods.
Ungar and David M.
There are a number of variants to it including the Averaged Learning Subspace Method ALSM.
The decision surfaces in all these methods are quadratic.
In some applications, we may require decision surfaces which are more nonlinear in nature.
In this paper, we have proposed the use of more than one subspace cluster in the representation of the classes in the subspace methods of pattern recognition.
By keeping the number of principal components in all the clusters the same, this model allows for a piecewise linear approximation of the decision surfaces.
We have used this model to extend both the SPRM and the ALSM to obtain the Extended SPRM and the Extended ALSM, подробнее на этой странице />We have investigated the use of a dynamic clustering algorithm for the assignment of patterns to different clusters as opposed to a random assignment.
We have conducted experiments on three data sets including a 192-dimensional large horse betting statistical approach data set.
The results indicate that the proposed methods have the potential to approximate any decision surface, and can considerably improve the classification accuracy on the test sets.
This is generally known as dimensionality reduction.
A very interesting strategy for dimensionality reduction is what is known as subspace analysis.
Beginning with the Eigenface method, face recognition and in general computer vision has witnessed a growing interest in algorithms that capitalize on this idea and an ample number of such efficient algorithms have been proposed.
These algorithms mainly differ in the kind of projection method used linear or non­linear or in the criterion employed for classification.
The objective of this paper is to provide a comprehensive performance evaluation of about twenty five different subspace algorithms under several important real time test conditions.
For this purpose, we have considered the performance of these algorithms on data taken from four standard face and object databases namely ORL, Yale, FERET and the COIL­20 object database.
This paper also presents some theoretical aspects of the algorithm and the analysis of the simulations carried out.
IT-32 4pp.
IT-30 4pp.
IT-29 5pp.
Ritter and Thomas M.
Martinets and Klaus J.
Paul Pauca and Robert J.
NMF is a dimensional reduction method and effective for document clustering, because a term-document matrix is high-dimensional and sparse.
The initial matrix of the NMF algorithm is regarded as a clustering result, therefore we can use NMF as a refinement method.
First we perform min-max cut Mcutwhich is a powerful spectral clustering method, and then refine the result via NMF.
Finally we should obtain an accurate clustering result.
However, NMF often fails to improve the given clustering result.
To overcome this problem, we use the Mcut object function to stop the iteration of NMF.
Series B Methodological Vol.
Sutton and Andrew G.
Series B Methodological Vol.
Weinberger and Jorma J.
Willems and Yuri M.
Shtarkov and Tjalling J.
Xing and Andrew Y.
Ng and Michael I.
A Small Sample Optimality Property of the M.
E79-A 5pp.
McNee and Joseph A.
Zinnes and Richard A.
IT-24 5pp.
IT-23 3pp.
J80-D-II 1pp.
J92-D 11pp.
J93-D 6pp.
J92-D 3pp.
J92-D 6pp.
J91-D 6pp.
J86-D-II 7pp.

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