Impact of Outlier Removal and Normalization
... that causes some instances to bear a stronger resemblance to one another than they do to the remaining instances." Clustering is one solution to the case of unsupervised learning, where class labeling information of the data is not available. It is a method where data is divided into groups (cluster ...
... that causes some instances to bear a stronger resemblance to one another than they do to the remaining instances." Clustering is one solution to the case of unsupervised learning, where class labeling information of the data is not available. It is a method where data is divided into groups (cluster ...
Parameter synthesis for probabilistic real-time systems
... − new construct constfilter (min, x1*x2, phi) − filters over parameter values, rather than states ...
... − new construct constfilter (min, x1*x2, phi) − filters over parameter values, rather than states ...
Training Products of Experts by Minimizing Contrastive Divergence
... to data using EM or gradient ascent and, if the individual models di er a lot, the mixture is likely to be a better t to the true distribution of the data than a random choice among the individual models. Indeed, if suÆciently many models are included in the mixture, it is possible to approximate c ...
... to data using EM or gradient ascent and, if the individual models di er a lot, the mixture is likely to be a better t to the true distribution of the data than a random choice among the individual models. Indeed, if suÆciently many models are included in the mixture, it is possible to approximate c ...
- VTUPlanet
... distribution functions. The method is built on the following ideas: (1) the influence of each data point can be formally modelled using a mathematical function, called an influence function, which describes the impact of a data point within its neighbourhood; (2) the overall density of the data spac ...
... distribution functions. The method is built on the following ideas: (1) the influence of each data point can be formally modelled using a mathematical function, called an influence function, which describes the impact of a data point within its neighbourhood; (2) the overall density of the data spac ...
KACU: K-means with Hardware Centroid
... objects with Gaussian distribution and be grouped into five clusters. The following experiments are conducted to compare the clock consumption between SPA with software centroid updating and KACU, both in continuous K-means algorithm. Note that the number of iterations depends on the method used for ...
... objects with Gaussian distribution and be grouped into five clusters. The following experiments are conducted to compare the clock consumption between SPA with software centroid updating and KACU, both in continuous K-means algorithm. Note that the number of iterations depends on the method used for ...
Approximate Planning in POMDPs with Macro
... grid point g multiple times so that it can approximate the probability distribution over the resulting belief-states b00 . Finally, it can update the estimated value of the grid point g and execute the macro-action chosen from the true belief state b. The process repeats from the next true belief st ...
... grid point g multiple times so that it can approximate the probability distribution over the resulting belief-states b00 . Finally, it can update the estimated value of the grid point g and execute the macro-action chosen from the true belief state b. The process repeats from the next true belief st ...
Scalable Methods for Estimating Document Frequencies
... Database of 2000 newsgroup articles Evaluated on a lexicon of 100 words Vary sample size s and number of queries q ...
... Database of 2000 newsgroup articles Evaluated on a lexicon of 100 words Vary sample size s and number of queries q ...
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.