Thomas Bayes versus the wedge model: An example inference prior
... density to seismic AVAZ (amplitude variation with azimuth). The PDFs of the rock properties such as crack density, crack aspect ratio, fluid properties in the cracks were known and the JPDF of P-wave speed, S-wave speed, and density are derived from log data. Monte Carlo sampling was used to populat ...
... density to seismic AVAZ (amplitude variation with azimuth). The PDFs of the rock properties such as crack density, crack aspect ratio, fluid properties in the cracks were known and the JPDF of P-wave speed, S-wave speed, and density are derived from log data. Monte Carlo sampling was used to populat ...
Radial Basis Function (RBF) Networks
... • The P-nearest neighbour algorithm with P set to 2 is used to find the size if the radii. • In each of the neurons, the distances to the other three neurons is 1, 1 and 1.414, so the two nearest cluster centres are at a distance of 1. • Using the mean squared distance as the radii gives each neuro ...
... • The P-nearest neighbour algorithm with P set to 2 is used to find the size if the radii. • In each of the neurons, the distances to the other three neurons is 1, 1 and 1.414, so the two nearest cluster centres are at a distance of 1. • Using the mean squared distance as the radii gives each neuro ...
Clustering Algorithms for Radial Basis Function Neural
... we need to re-calculate k new centroids as barycenters of the clusters resulting from the previous step. After we have these k new centroids, a new binding has to be done between the same data set points and the nearest new centroid. A loop has been generated. As a result of this loop we may notice ...
... we need to re-calculate k new centroids as barycenters of the clusters resulting from the previous step. After we have these k new centroids, a new binding has to be done between the same data set points and the nearest new centroid. A loop has been generated. As a result of this loop we may notice ...
Recommending Services using Description Similarity Based Clustering and Collaborative Filtering
... (RSs) are techniques which is intelligent applications, Where they want to choose some items among a set of alternative products or services.RSs handles two main challenges for big data application: 1) making decision within acceptable time; and 2) generating ideal recommendations from so many servi ...
... (RSs) are techniques which is intelligent applications, Where they want to choose some items among a set of alternative products or services.RSs handles two main challenges for big data application: 1) making decision within acceptable time; and 2) generating ideal recommendations from so many servi ...
A hybrid projection based and radial basis function architecture
... units is assigned to one of the cluster centers. The clustering can be done by a k-means procedure [3]. A discussion about the benefits of more recent approaches to clustering is beyond the scope of this paper. Unlike Orr [11], we assume that the clusters are symmetric, although each cluster may hav ...
... units is assigned to one of the cluster centers. The clustering can be done by a k-means procedure [3]. A discussion about the benefits of more recent approaches to clustering is beyond the scope of this paper. Unlike Orr [11], we assume that the clusters are symmetric, although each cluster may hav ...
A Parameter-Free Classification Method for Large Scale Learning
... within each output class, and solely relies on the estimation of univariate conditional probabilities. The evaluation of these probabilities for numerical variables has already been discussed in the literature (Dougherty et al., 1995; Liu et al., 2002). Experiments demonstrate that even a simple equ ...
... within each output class, and solely relies on the estimation of univariate conditional probabilities. The evaluation of these probabilities for numerical variables has already been discussed in the literature (Dougherty et al., 1995; Liu et al., 2002). Experiments demonstrate that even a simple equ ...
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Multiple Regression
... regression model with p independent variables fitted to a data set with n observations is: ...
... regression model with p independent variables fitted to a data set with n observations is: ...
slides - Chrissnijders
... be able to calculate a best-fitting line (only for the estimates of the confidence intervals we need that). With maximum likelihood estimation we need this from the start ...
... be able to calculate a best-fitting line (only for the estimates of the confidence intervals we need that). With maximum likelihood estimation we need this from the start ...
Automatic Labeling of Multinomial Topic Models
... Score' (l , i ) Score(l , i ) Score(l ,1,..., i 1,i 1,..., k ) ...
... Score' (l , i ) Score(l , i ) Score(l ,1,..., i 1,i 1,..., k ) ...
Enhancing K-means Clustering Algorithm with Improved Initial Center
... used, one method for finding the better initial centroids. And another method for an efficient way for assigning data points to appropriate clusters. In the paper [2] the method used for finding the initial centroids computationally expensive. In this paper we proposed a new approach for finding the ...
... used, one method for finding the better initial centroids. And another method for an efficient way for assigning data points to appropriate clusters. In the paper [2] the method used for finding the initial centroids computationally expensive. In this paper we proposed a new approach for finding the ...
Expectation–maximization algorithm
In statistics, an expectation–maximization (EM) algorithm is an iterative method for finding maximum likelihood or maximum a posteriori (MAP) estimates of parameters in statistical models, where the model depends on unobserved latent variables. The EM iteration alternates between performing an expectation (E) step, which creates a function for the expectation of the log-likelihood evaluated using the current estimate for the parameters, and a maximization (M) step, which computes parameters maximizing the expected log-likelihood found on the E step. These parameter-estimates are then used to determine the distribution of the latent variables in the next E step.