IOSR Journal of Computer Engineering (IOSR-JCE)
... against compromised mobile nodes, which often carry the private keys. Integrity validation using redundant information (from different nodes), such as those being used in secure routing, also relies on the trustworthiness of other nodes, which could likewise be a weak link for sophisticated attacks. ...
... against compromised mobile nodes, which often carry the private keys. Integrity validation using redundant information (from different nodes), such as those being used in secure routing, also relies on the trustworthiness of other nodes, which could likewise be a weak link for sophisticated attacks. ...
An Online and Approximate Solver for POMDPs with Continuous
... Key to the tremendous advances in both offline and online POMDP-based planning is the use of sampling to trade optimality with approximate optimality in exchange for speed. Sampling based planners reduce the complexity of planning in the belief space B by representing B as a set of sampled beliefs a ...
... Key to the tremendous advances in both offline and online POMDP-based planning is the use of sampling to trade optimality with approximate optimality in exchange for speed. Sampling based planners reduce the complexity of planning in the belief space B by representing B as a set of sampled beliefs a ...
android short messages filtering for bahasa using
... developed a system that could classify SMS between SMS spam with not spam (ham) in Bahasa (Indonesian Language). This system conducted with Multinomial Naïve Bayes classification with the feature weighting Term Frequency - Inverse Document Frequency (TF-IDF). Before the classification, data had been ...
... developed a system that could classify SMS between SMS spam with not spam (ham) in Bahasa (Indonesian Language). This system conducted with Multinomial Naïve Bayes classification with the feature weighting Term Frequency - Inverse Document Frequency (TF-IDF). Before the classification, data had been ...
What Is Clustering
... Grouping data based on probability density models: based on how many (possibly weighted) features are the same. COBWEB (Fisher’87) Assumption: The probability distribution on different attributes are independent of each other --- This is often too strong because correlation may exist between attribu ...
... Grouping data based on probability density models: based on how many (possibly weighted) features are the same. COBWEB (Fisher’87) Assumption: The probability distribution on different attributes are independent of each other --- This is often too strong because correlation may exist between attribu ...
Yannis_Kevrekidis-Presentation
... The nonlinear partial differential equations of mass, momentum, energy, Species and charge transport…. can be solved in terms of functions of limited differentiability, no more than the physics warrants, rather than the analytic functions of classical analysis… ….. basis sets consisting of low-order ...
... The nonlinear partial differential equations of mass, momentum, energy, Species and charge transport…. can be solved in terms of functions of limited differentiability, no more than the physics warrants, rather than the analytic functions of classical analysis… ….. basis sets consisting of low-order ...
A Framework for Categorize Feature Selection Algorithms
... Bulletin de la Société Royale des sciences de Liège, Vol. 85, 2016, p. 850 - 862 are applied in methods of feature selection: (i) independent criteria: They are independently of the algorithm used. The main types of these criteria are: 1) distance measure, also known as separatability, provides a m ...
... Bulletin de la Société Royale des sciences de Liège, Vol. 85, 2016, p. 850 - 862 are applied in methods of feature selection: (i) independent criteria: They are independently of the algorithm used. The main types of these criteria are: 1) distance measure, also known as separatability, provides a m ...
Data Mining and Knowledge Discovery Practice notes: Numeric
... 7. Why does Naïve Bayes work well (even if independence assumption is clearly violated)? 8. What are the benefits of using Laplace estimate instead of relative frequency for probability estimation in Naïve Bayes? ...
... 7. Why does Naïve Bayes work well (even if independence assumption is clearly violated)? 8. What are the benefits of using Laplace estimate instead of relative frequency for probability estimation in Naïve Bayes? ...
Missing Data in Educational Research: A Review of Reporting
... city. Assuming these factors were unrelated to other measured variables such as socioeconomic status, the observed scores represent a random sample of the hypothetically complete data set. In certain circumstances MCAR missing data might even be a purposive byproduct of the data collection procedure ...
... city. Assuming these factors were unrelated to other measured variables such as socioeconomic status, the observed scores represent a random sample of the hypothetically complete data set. In certain circumstances MCAR missing data might even be a purposive byproduct of the data collection procedure ...
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.