Mining recent temporal patterns for event detection in
... task). Examples of such problems are the detection of adverse medical events (e.g. drug toxicity) in clinical data [10], detection of the equipment malfunction [9], fraud detection [23], environmental monitoring [16], intrusion detection [7] and others. Given that class labels are associated with sp ...
... task). Examples of such problems are the detection of adverse medical events (e.g. drug toxicity) in clinical data [10], detection of the equipment malfunction [9], fraud detection [23], environmental monitoring [16], intrusion detection [7] and others. Given that class labels are associated with sp ...
Applied Generalized Linear Mixed Models: Continuous and Discrete
... Multiple regression and ANOVA dominated statistical analysis of data in the social and behavioral sciences for many years. The recognition that multiple regression and ANOVA are special cases of a more general model, the general linear model, was known for many years by statisticians, but it was not ...
... Multiple regression and ANOVA dominated statistical analysis of data in the social and behavioral sciences for many years. The recognition that multiple regression and ANOVA are special cases of a more general model, the general linear model, was known for many years by statisticians, but it was not ...
SPSS Regression 17.0
... on only a limited number of values—so that there are several cases at each distinct covariate pattern—the subpopulation approach can produce valid goodness-of-fit tests and informative residuals, while the individual case level approach cannot. Logistic Regression provides the following unique featu ...
... on only a limited number of values—so that there are several cases at each distinct covariate pattern—the subpopulation approach can produce valid goodness-of-fit tests and informative residuals, while the individual case level approach cannot. Logistic Regression provides the following unique featu ...
ISpaper04 July 07
... simulation, as well as soft computing techniques are commonly used for this purpose. In the decision making process, one often needs to introduce soft computing techniques in order to understand the structure and the behavior of a system that is highly nonlinear and highly uncertain. Amongst the sof ...
... simulation, as well as soft computing techniques are commonly used for this purpose. In the decision making process, one often needs to introduce soft computing techniques in order to understand the structure and the behavior of a system that is highly nonlinear and highly uncertain. Amongst the sof ...
Efficient Frequent Pattern Mining
... During the past few years, several very good books and surveys have been published on these topics, to which we refer the interested reader for more information [43, 39, 47]. In this thesis we focus on the Frequent Pattern Discovery task and how it can be efficiently solved in the specific context o ...
... During the past few years, several very good books and surveys have been published on these topics, to which we refer the interested reader for more information [43, 39, 47]. In this thesis we focus on the Frequent Pattern Discovery task and how it can be efficiently solved in the specific context o ...
Multivariate Data Analysis
... Initialization: an arbitrary value for the coefficients (usually 0). log-likelihood is computed and variation of coefficients values observed. Iteration is then performed until ` is maximum (equivalent to maximizing L). The results are the maximum likelihood estimates of α and β and estimates of P(y ...
... Initialization: an arbitrary value for the coefficients (usually 0). log-likelihood is computed and variation of coefficients values observed. Iteration is then performed until ` is maximum (equivalent to maximizing L). The results are the maximum likelihood estimates of α and β and estimates of P(y ...
Multi-threaded Implementation of Association Rule Mining with
... patterns and also generate the datasets that contain the transactions. These transactions can be further mined to find the interesting patterns. Most Associated Sequential Pattern is different from traditional association rule mining algorithms which deals with generating frequent patterns along wit ...
... patterns and also generate the datasets that contain the transactions. These transactions can be further mined to find the interesting patterns. Most Associated Sequential Pattern is different from traditional association rule mining algorithms which deals with generating frequent patterns along wit ...
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.