IOSR Journal of Computer Engineering (IOSR-JCE)
... popular compared to the other conventional models due to their computational efficiency. The main variation between grid-based and other clustering methods is as follows. In grid-based clustering all the clustering operations are performed on the segmented data space, rather than the original data o ...
... popular compared to the other conventional models due to their computational efficiency. The main variation between grid-based and other clustering methods is as follows. In grid-based clustering all the clustering operations are performed on the segmented data space, rather than the original data o ...
An Overview of Machine Learning and Pattern Recognition
... The US financial industry collectively generates tens of billions of records every day. Everything from order entry, order routes, order executions, quotes, trades, etc., everything is logged. Regulatory agencies have access to large percentage of this data—with the sole purpose of regulating market ...
... The US financial industry collectively generates tens of billions of records every day. Everything from order entry, order routes, order executions, quotes, trades, etc., everything is logged. Regulatory agencies have access to large percentage of this data—with the sole purpose of regulating market ...
Multiple Linear Regression in Data Mining
... drop the assumption of normality (Assumption 5) and allow the noise variables to follow arbitrary distributions, these estimates are very good for prediction. We can show that predictions based on these estimates are the best linear predictions in that they minimize the expected squared error. In ot ...
... drop the assumption of normality (Assumption 5) and allow the noise variables to follow arbitrary distributions, these estimates are very good for prediction. We can show that predictions based on these estimates are the best linear predictions in that they minimize the expected squared error. In ot ...
How to Be an Intelligent TA Expert
... • Strong Buy / Weak Buy/ Weak Sell / Strong Sell B. Regression: a continuous quantity (linear regression) • Future % increase in the market • Predicted amount of future purchases ...
... • Strong Buy / Weak Buy/ Weak Sell / Strong Sell B. Regression: a continuous quantity (linear regression) • Future % increase in the market • Predicted amount of future purchases ...
assoc - CSE, IIT Bombay
... Number of distinct items: tens of thousands Lots of work on scalable algorithms Typically two parts to the algorithm: Finding all frequent itemsets with support > S 2. Finding rules with confidence greater than C ...
... Number of distinct items: tens of thousands Lots of work on scalable algorithms Typically two parts to the algorithm: Finding all frequent itemsets with support > S 2. Finding rules with confidence greater than C ...
Data Warehouses and Bayesian Analysis - A Match Made by SAS
... value of the parameter being evaluated (judgemental priors). In either case, the priors will have to be evaluated and formulated before any further analysis based on them can be done. In this conceptual paper, I will explain with details Bayesian analysis, prior and posterior distributions and how t ...
... value of the parameter being evaluated (judgemental priors). In either case, the priors will have to be evaluated and formulated before any further analysis based on them can be done. In this conceptual paper, I will explain with details Bayesian analysis, prior and posterior distributions and how t ...
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.