Clustering distributed sensor data streams using local
... fade out with the frequent state monitoring step. The first layer is initialized based on the number of intervals pi (that should be much larger than the desired final number of intervals wi ) and the range of the variable. The range of the variable is only indicative, as it is used to define the in ...
... fade out with the frequent state monitoring step. The first layer is initialized based on the number of intervals pi (that should be much larger than the desired final number of intervals wi ) and the range of the variable. The range of the variable is only indicative, as it is used to define the in ...
Numerical Methods
... The value of a function at x + h is given in terms of the values of derivatives of the function at x The general idea is to use a small number of terms in this series to approximate a solution. ...
... The value of a function at x + h is given in terms of the values of derivatives of the function at x The general idea is to use a small number of terms in this series to approximate a solution. ...
Study on Feature Selection Methods for Text Mining
... methods. Most text categorization techniques reduce this large number of features by eliminating stopwords, or stemming. This is effective to a certain extent but the remaining number of features is still huge. It is important to use feature selection methods to handle the high dimensionality of dat ...
... methods. Most text categorization techniques reduce this large number of features by eliminating stopwords, or stemming. This is effective to a certain extent but the remaining number of features is still huge. It is important to use feature selection methods to handle the high dimensionality of dat ...
isda.softcomputing.net
... algorithm is to reduce the number of database scans required for the updating process. In practice, the incremental algorithm is not invoked every time a transaction is added to the database. However, it is invoked after a non-trivial number of transactions are added. In our case, the proposed algor ...
... algorithm is to reduce the number of database scans required for the updating process. In practice, the incremental algorithm is not invoked every time a transaction is added to the database. However, it is invoked after a non-trivial number of transactions are added. In our case, the proposed algor ...
Training Iterative Collective Classifiers with Back-Propagation
... phenomena. For example, when classifying individuals by their personality traits in a social network, a common pattern is that individuals will communicate with like-minded individuals, suggesting that predicted labels should also tend to be uniform among connected nodes. Collective classification m ...
... phenomena. For example, when classifying individuals by their personality traits in a social network, a common pattern is that individuals will communicate with like-minded individuals, suggesting that predicted labels should also tend to be uniform among connected nodes. Collective classification m ...
Optimization of Naïve Bayes Data Mining Classification Algorithm
... algorithms have been implemented, used and compared for different data domains, however, there has been no single algorithm found to be superior over all others for all data sets for different domain. Naive Bayesian classifier represents each class with a probabilistic summary and finds the most lik ...
... algorithms have been implemented, used and compared for different data domains, however, there has been no single algorithm found to be superior over all others for all data sets for different domain. Naive Bayesian classifier represents each class with a probabilistic summary and finds the most lik ...
GigaTensor: Scaling Tensor Analysis Up By 100 Times
... dataset, described in Section 4, that we are using in this work; this dataset consists of about 26 · 106 noun-phrases (and for a moment, ignore the number of the “context" phrases, which account for the third mode). Then, one of the intermediate matrices will have an explosive dimension of ≈ 7 · 101 ...
... dataset, described in Section 4, that we are using in this work; this dataset consists of about 26 · 106 noun-phrases (and for a moment, ignore the number of the “context" phrases, which account for the third mode). Then, one of the intermediate matrices will have an explosive dimension of ≈ 7 · 101 ...
GigaTensor: Scaling Tensor Analysis Up By 100 Times
... dataset, described in Section 4, that we are using in this work; this dataset consists of about 26 · 106 noun-phrases (and for a moment, ignore the number of the “context" phrases, which account for the third mode). Then, one of the intermediate matrices will have an explosive dimension of ≈ 7 · 101 ...
... dataset, described in Section 4, that we are using in this work; this dataset consists of about 26 · 106 noun-phrases (and for a moment, ignore the number of the “context" phrases, which account for the third mode). Then, one of the intermediate matrices will have an explosive dimension of ≈ 7 · 101 ...
On Data Mining and Classification Using a Bayesian
... which is our main method to gain knowledge about this world, is based upon sampling a subspace of events on which hypotheses can be tested and theories be built. These events can be measured in probabilities and rules can be deduced about relations between different events. To measure probabilities, ...
... which is our main method to gain knowledge about this world, is based upon sampling a subspace of events on which hypotheses can be tested and theories be built. These events can be measured in probabilities and rules can be deduced about relations between different events. To measure probabilities, ...
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.