IOSR Journal of Computer Engineering (IOSR-JCE)
... creatures, or phenotypes) to an optimization problem evolves toward better solutions. Traditionally, solutions are represented in binary as strings of 0s and 1s, but other encodings are also possible. The evolution usually starts from a population of randomly generated individuals and happens in gen ...
... creatures, or phenotypes) to an optimization problem evolves toward better solutions. Traditionally, solutions are represented in binary as strings of 0s and 1s, but other encodings are also possible. The evolution usually starts from a population of randomly generated individuals and happens in gen ...
A Survey on: Stratified mapping of Microarray Gene Expression
... R. C. Barros, R. Cerri, P. A. Jaskowiak, and A. C. P. L. F. de Carvalho [18] proposed hill-climbing bottomup induction an iterative searching methodology. Hill climbing is simply a loop that continually moves in the direction of increasing value. The algorithm does not maintain a search tree, so the ...
... R. C. Barros, R. Cerri, P. A. Jaskowiak, and A. C. P. L. F. de Carvalho [18] proposed hill-climbing bottomup induction an iterative searching methodology. Hill climbing is simply a loop that continually moves in the direction of increasing value. The algorithm does not maintain a search tree, so the ...
INSURANCE FRAUD The Crime and Punishment
... Modeling hidden risk exposures as additional dimension(s) of the loss severity distribution via EM, Expectation-Maximization, Algorithm Considering the mixtures of probability distributions as the model for losses affected by hidden exposures with some parameters of the mixtures considered missi ...
... Modeling hidden risk exposures as additional dimension(s) of the loss severity distribution via EM, Expectation-Maximization, Algorithm Considering the mixtures of probability distributions as the model for losses affected by hidden exposures with some parameters of the mixtures considered missi ...
A Multiobjective Genetic Algorithm for Attribute Selection
... At each generation, selection is performed as follows. First the GA selects all the non-dominated individuals (the Pareto front) of the current population. These non-dominated individuals are passed unaltered to the next generation by elitism [1]. Let N be the population size, and let Nelit be the n ...
... At each generation, selection is performed as follows. First the GA selects all the non-dominated individuals (the Pareto front) of the current population. These non-dominated individuals are passed unaltered to the next generation by elitism [1]. Let N be the population size, and let Nelit be the n ...
Implementing Improved Algorithm Over APRIORI Data Mining
... important algorithm for mining frequent itemsets. Apriori is used to find all frequent itemsets in a given database DB. The key idea of Apriori algorithm is to make multiple passes over the database. It employs an iterative approach known as a breadth-first search (level-wise search) through the sea ...
... important algorithm for mining frequent itemsets. Apriori is used to find all frequent itemsets in a given database DB. The key idea of Apriori algorithm is to make multiple passes over the database. It employs an iterative approach known as a breadth-first search (level-wise search) through the sea ...
Slide 1
... Pattern Language Different types of pattern languages are used to represent patterns of different types: Sequence models: regular expressions, hidden Markov models, stochastic context-free grammars Structural models: 3-D coordinates Phylogeny models: trees and cladograms Network models: boo ...
... Pattern Language Different types of pattern languages are used to represent patterns of different types: Sequence models: regular expressions, hidden Markov models, stochastic context-free grammars Structural models: 3-D coordinates Phylogeny models: trees and cladograms Network models: boo ...
Scalable Sequential Spectral Clustering
... quadratic space and time because of the computation of pairwise distance of data points. This process is easy to sequentialize. Specifically, we can keep only one sample of data xi in the memory and then load all the other data from the disk sequentially and compute the distances from xi to all the o ...
... quadratic space and time because of the computation of pairwise distance of data points. This process is easy to sequentialize. Specifically, we can keep only one sample of data xi in the memory and then load all the other data from the disk sequentially and compute the distances from xi to all the o ...
a novel approach for frequent pattern mining
... (Knowledge Discovery from Data) [10]. Data mining is to find valid, novel, potentially useful and ultimately understandable patterns in data. In general there are many kinds of patterns that can be discovered from data . For example, association rules can be mined for market basket analysis, classif ...
... (Knowledge Discovery from Data) [10]. Data mining is to find valid, novel, potentially useful and ultimately understandable patterns in data. In general there are many kinds of patterns that can be discovered from data . For example, association rules can be mined for market basket analysis, classif ...
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.