Genetic and Evolutionary Computation Conference 2008
... Dr. Maarten Keijzer to thank for ensuring that everyone stuck with their deadlines. The third, and most important part of GECCO’s success is its attendees. A conference can only be as good as those attending it, and GECCO has been fortunate enough to attract a wonderful mix of innovation, curiosity ...
... Dr. Maarten Keijzer to thank for ensuring that everyone stuck with their deadlines. The third, and most important part of GECCO’s success is its attendees. A conference can only be as good as those attending it, and GECCO has been fortunate enough to attract a wonderful mix of innovation, curiosity ...
Governing Algorithms: A Provocation Piece
... grandiose claims about purportedly “new” and “revolutionary” technologies? What explains their sudden claim to fame? Why have commentators rallied around this term? 5. In 2004, Lorraine Daston called for research “devoted to the history and mythology (in the sense of Roland Barthes) of the algorithm ...
... grandiose claims about purportedly “new” and “revolutionary” technologies? What explains their sudden claim to fame? Why have commentators rallied around this term? 5. In 2004, Lorraine Daston called for research “devoted to the history and mythology (in the sense of Roland Barthes) of the algorithm ...
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... hierarchical dirichlet processes for MTLVM, which can dynamically generate recruitment topics. Finally, we implement an intelligent prototype system to empirically evaluate our approach based on a real-world recruitment data set collected from China for the time period from 2014 to 2015. Indeed, by ...
... hierarchical dirichlet processes for MTLVM, which can dynamically generate recruitment topics. Finally, we implement an intelligent prototype system to empirically evaluate our approach based on a real-world recruitment data set collected from China for the time period from 2014 to 2015. Indeed, by ...
Bayesian Variable Selection in Normal Regression Models
... on which all inference is based on. For the result of a Bayesian analysis the shape of the prior on the regression coefficients might be influential. If the prime interest of the analysis is coefficient estimation, the prior should be located over the a-priori guess value of the coefficient. If howe ...
... on which all inference is based on. For the result of a Bayesian analysis the shape of the prior on the regression coefficients might be influential. If the prime interest of the analysis is coefficient estimation, the prior should be located over the a-priori guess value of the coefficient. If howe ...
DISC: Data-Intensive Similarity Measure for Categorical Data
... are not inherently ordered and hence a notion of direct comparison between two categorical values is not possible. In addition, the notion of similarity can differ depending on the particular domain, dataset, or task at hand. Although there is no inherent ordering in categorical data, there are othe ...
... are not inherently ordered and hence a notion of direct comparison between two categorical values is not possible. In addition, the notion of similarity can differ depending on the particular domain, dataset, or task at hand. Although there is no inherent ordering in categorical data, there are othe ...
Research Article Classification of Textual E-Mail Spam
... Genetic algorithms are powerful tools for solving large dimension problems. But they do not guarantee an optimality of a found solution. In genetic algorithms, the first step is an encoding of solutions in the form of chromosomes which depends on the character of a solved problem. Therefore, before ...
... Genetic algorithms are powerful tools for solving large dimension problems. But they do not guarantee an optimality of a found solution. In genetic algorithms, the first step is an encoding of solutions in the form of chromosomes which depends on the character of a solved problem. Therefore, before ...
Mining Frequent Patterns with Counting Inference
... patterns in a levelwise manner. During each iteration corresponding to a level, a set of candidate patterns is created by joining the frequent patterns discovered during the previous iteration, the supports of all candidate patterns are counted and infrequent ones are discarded. The most prominent a ...
... patterns in a levelwise manner. During each iteration corresponding to a level, a set of candidate patterns is created by joining the frequent patterns discovered during the previous iteration, the supports of all candidate patterns are counted and infrequent ones are discarded. The most prominent a ...
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.