A WK-Means Approach for Clustering
... efficient clustering methods is K-means, as it has linear time complexity and is simple to implement. However, it suffers from gets trapped in local optima. Therefore, many methods have been produced by hybridizing K-means and other methods. In this paper, we propose a hybrid method that hybridizes ...
... efficient clustering methods is K-means, as it has linear time complexity and is simple to implement. However, it suffers from gets trapped in local optima. Therefore, many methods have been produced by hybridizing K-means and other methods. In this paper, we propose a hybrid method that hybridizes ...
NBER WORKING PAPER SERIES ESTIMATING THE COVARIATES OF HISTORICAL HEIGHTS Kenneth Wachter
... fact that adult heights are normally distributed. With the knowledge that the deficient sample was drawn from an underlying normal distribution, we ...
... fact that adult heights are normally distributed. With the knowledge that the deficient sample was drawn from an underlying normal distribution, we ...
Dummy Dependent Variables Models
... that is, L, the log of the odds ration, is not only linear in x, but also linear in the parameters. L is called the logit, and hence the name logit model. Logit model cannot be estimated using OLS. Instead, we use MLE that discussed previous section, an iterative estimation technique that is especia ...
... that is, L, the log of the odds ration, is not only linear in x, but also linear in the parameters. L is called the logit, and hence the name logit model. Logit model cannot be estimated using OLS. Instead, we use MLE that discussed previous section, an iterative estimation technique that is especia ...
MDMV Visualization
... Example: 7 independent variables + each has 10 values = 10,000,000 points Need: – hierarchical subspace zooming to reduce dimension ...
... Example: 7 independent variables + each has 10 values = 10,000,000 points Need: – hierarchical subspace zooming to reduce dimension ...
u| z
... Obtaining Practical Bayesian Estimates -- The Conditional Mode For problems of realistic size the conditional PDF f u|z(u| z) is difficult to derive in closed form and is too large to store in numerical form. Even when this PDF can be computed, it is difficult to interpret. Usually spatial plots of ...
... Obtaining Practical Bayesian Estimates -- The Conditional Mode For problems of realistic size the conditional PDF f u|z(u| z) is difficult to derive in closed form and is too large to store in numerical form. Even when this PDF can be computed, it is difficult to interpret. Usually spatial plots of ...
Delay Differential Equations
... If hn is bigger than a lag τj , one of the arguments of y(tn + αm hn − τj ) may be bigger than tn . The term is then not defined and the “explicit” RK formula is implicit. Some codes limit hn to make the formula explicit, but this can be very inefficient. Predict values for the delayed terms using t ...
... If hn is bigger than a lag τj , one of the arguments of y(tn + αm hn − τj ) may be bigger than tn . The term is then not defined and the “explicit” RK formula is implicit. Some codes limit hn to make the formula explicit, but this can be very inefficient. Predict values for the delayed terms using t ...
Interactive HMM construction based on interesting sequences
... whose updates are available on the intranet. The most interesting sequence was sophos,sophos; it’s probability in data was 11.48% while the initial model predicted it to be only 1.17%. The second most interesting sequence was one in which the sophos directory has been accessed four times. It is inte ...
... whose updates are available on the intranet. The most interesting sequence was sophos,sophos; it’s probability in data was 11.48% while the initial model predicted it to be only 1.17%. The second most interesting sequence was one in which the sophos directory has been accessed four times. It is inte ...
Mining_vehicleTrajec.. - Computer Engineering
... The implementation was made using reading a video file and extracting information frame by frame, with any assumptions to obtain mostly good data. In each frame we detect the vehicles and its center. ...
... The implementation was made using reading a video file and extracting information frame by frame, with any assumptions to obtain mostly good data. In each frame we detect the vehicles and its center. ...
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.