MCAIM: Modified CAIM Discretization Algorithm for Classification
... 3.5 Result Analysis Table 2 depicts the results of six discretization methods for seven different data sets. Among six discretization methods, Equal-width, equal-count and standard deviation are unsupervised discretization algorithms [22]. A typical problem of unsupervised method is that it is diffi ...
... 3.5 Result Analysis Table 2 depicts the results of six discretization methods for seven different data sets. Among six discretization methods, Equal-width, equal-count and standard deviation are unsupervised discretization algorithms [22]. A typical problem of unsupervised method is that it is diffi ...
“What-If” Deployment and Configuration Questions with WISE
... scenario; (4) estimating the response-time function and distribution. Each of these tasks raises a number of challenges, some of which are general problems with applying statistical learning in practice, and others are specific to what-if scenario evaluation. This section provides an overview and ne ...
... scenario; (4) estimating the response-time function and distribution. Each of these tasks raises a number of challenges, some of which are general problems with applying statistical learning in practice, and others are specific to what-if scenario evaluation. This section provides an overview and ne ...
Mining Sequential Patterns - VTT Virtual project pages
... An algorithm for mining sequential patterns. A timing constraint. The maximum difference of the first and last event of a sequential pattern. An algorithm for mining sequential patterns. An algorithm for mining sequential patterns. A timing constraint. Minimum gap between the first event of an eleme ...
... An algorithm for mining sequential patterns. A timing constraint. The maximum difference of the first and last event of a sequential pattern. An algorithm for mining sequential patterns. An algorithm for mining sequential patterns. A timing constraint. Minimum gap between the first event of an eleme ...
Ant-based Clustering Algorithms: A Brief Survey
... The clustering problem is the ordering of a set of data into groups, based on one or more features of the data. Cluster analysis [15] [39] [44] is an unsupervised learning method that constitutes a main role of an intelligent data analysis process. It is used for the exploration of inter-relationshi ...
... The clustering problem is the ordering of a set of data into groups, based on one or more features of the data. Cluster analysis [15] [39] [44] is an unsupervised learning method that constitutes a main role of an intelligent data analysis process. It is used for the exploration of inter-relationshi ...
IBM SPSS Regression 24
... categorical; if categorical, they should be dummy or indicator coded (there is an option in the procedure to recode categorical variables automatically). Assumptions. Logistic regression does not rely on distributional assumptions in the same sense that discriminant analysis does. However, your solu ...
... categorical; if categorical, they should be dummy or indicator coded (there is an option in the procedure to recode categorical variables automatically). Assumptions. Logistic regression does not rely on distributional assumptions in the same sense that discriminant analysis does. However, your solu ...
Finding Optimal Decision Trees
... decision tree algorithm, which allows more prunes. We will study theoretical and practical properties of this extended algorithm. In theory, we have described a class of distributions, for which we have proven that the optimal tree is always found by the algorithm. In practical tests, we have studie ...
... decision tree algorithm, which allows more prunes. We will study theoretical and practical properties of this extended algorithm. In theory, we have described a class of distributions, for which we have proven that the optimal tree is always found by the algorithm. In practical tests, we have studie ...
An Explorative Parameter Sweep: Spatial-temporal Data
... time series data into the frequency domain to extract information about present periods in the model. All these features could then be used to compare different simulation (with different parameter settings) with some proximity analysis (goal 2). An essential question will be; how will these feature ...
... time series data into the frequency domain to extract information about present periods in the model. All these features could then be used to compare different simulation (with different parameter settings) with some proximity analysis (goal 2). An essential question will be; how will these feature ...
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.