[PDF] Top 20 Regression via Clustering using Dirichlet Mixtures
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Regression via Clustering using Dirichlet Mixtures
... in an asymptotic sense. In fact, if there are more groups than the true one then the fitting within those groups does harm only with a higher variance. The number of ob- servations falling in the true groups tends to ... See full document
99
Stock Price Prediction Using Clustering and Regression: A Review
... companies using data mining approaches. The clustering and regression are the two techniques of data mining used here, Validation index is used for analyzing the performance of different ... See full document
5
Indoor Wlan Positioning Using Hybrid Svm Hyperplane Margin Clustering And Regression
... by using the Support Vector Machine (SVM) Hyperplane Margin Clustering and Regression ...Margin Clustering (SVMC) to reduce the search space of the fingerprint ...Vector Regression ... See full document
5
Pipeline failure prediction in water distribution networks using evolutionary polynomial regression combined with K-means clustering
... polynomial regression model must capture different failure patterns in the entire ...this clustering approach enhanced the failure prediction accuracy to a certain extent, a more precise clustering ... See full document
17
Improved Image Segmentation Using Dirichlet Process Multiple View Learning With K Means Clustering
... logistic regression classifiers for numerous tasks, for cases in which there is the prospective of enhancing individual-task performance via appropriate sharing of inter-task ... See full document
13
PReMiuM : an R package for profile regression mixture models using Dirichlet processes
... Bayesian clustering using a Dirich- let process mixture ...to regression models, non- parametrically linking a response vector to covariate data through cluster membership (Molitor, Papathomas, ... See full document
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Improving Prediction from Dirichlet Process Mixtures via Enrichment
... local clustering, that is, the clustering structure for the marginal of X and the regression of Y on x may be ...enriched Dirichlet process (EDP) developed in Wade et ...nested ... See full document
31
Improving prediction from Dirichlet process mixtures via enrichment
... local clustering, that is, the clustering structure for the marginal of X and the regression of Y on x may be ...enriched Dirichlet process (EDP) developed in Wade et ...nested ... See full document
32
Sensor based human activity mining using Dirichlet process mixtures of directional statistical models
... and clustering are the two mostly used techniques that support unsupervised activity ...and clustering algorithms to discover representative sensor events for ... See full document
10
Modeling and visualizing uncertainty in gene expression clusters using Dirichlet process mixtures
... hierarchical clustering rely on the setting of some score threshold to distinguish members of a particular cluster from nonmembers, making the determination of the number of clusters arbitrary and ...the ... See full document
15
Dirichlet multinomial mixtures : generative models for microbial metagenomics
... both clustering and classification of microbial ...the Dirichlet prior is that the community parameter vectors which are unobserved can be integrated out or margin- alised to give an analytic solution to ... See full document
16
Dirichlet Multinomial Mixtures: Generative Models for Microbial Metagenomics
... both clustering and classification of microbial ...the Dirichlet prior is that the community parameter vectors which are unobserved can be integrated out or margin- alised to give an analytic solution to ... See full document
15
Dirichlet Process Mixtures of Generalized Linear Models
... and regression trees (CART) (Brieman et ...handle regression with continuous, categorical or mixed ...a regression model within each leaf of the tree. Bayesian regression trees place a prior ... See full document
31
Nonlinear Models Using Dirichlet Process Mixtures
... We could assume that y and x are independent within components, and capture the dependence between the response and the covariates by clustering too. However, this may lead to poor per- formance (e.g., when ... See full document
22
Language Model Information Retrieval with Document Expansion
... We evaluated our method using six representative retrieval test sets. The experiment results show that document expansion smoothing consistently outper- forms the baseline smoothing methods in all the data sets. ... See full document
8
Topic Modeling with Wasserstein Autoencoders
... the Dirichlet prior directly via distribution matching, without any Gaussian approximation; by preserving the Dirichlet prior, our model repre- sents a much more faithful generalization of LDA to ... See full document
37
Automatic Classification for Various Images Collections Using Two Stages Clustering Method
... image clustering approach based on the image histogram, which is processed by the empirical mode decomposition ...image clustering. In this paper, we present an unsupervised clustering me- thod for ... See full document
6
A Bayesian Model for Supervised Clustering with the Dirichlet Process Prior
... supervised clustering problem, the generic prob- lem encountered in tasks such as reference matching, coreference resolution, identity uncertainty and record ...Our clustering model is based on the ... See full document
27
Unsupervised and Constrained Dirichlet Process Mixture Models for Verb Clustering
... symmetric Dirichlet prior with param- eters of 1 (λ in Equation ...obtained using NMF was 35. We run the Gibbs sampler 5 times, using 100 iterations for burn-in and draw 20 samples from each run with ... See full document
9
Speaker Recognition with Mixtures of Gaussians with Sparse Regression Matrices
... sparse regression matrices, it is known that the absence of an arc is equivalent to con- ditional independencies, yet the above criteria can only test for marginal ...of regression coef- ficients to be set ... See full document
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