QUIZ 7 - Penn Math
... p A(x) = x 9 − x2 , that is the function we have to maximize, for x between 0 (the triangle collapses to a vertical line) and 3 (the triangle collapses to a horizontal line). As the hint suggests, we can just maximize A2 (x) = x2 (9 − x2 ) = 9x2 − x4 . Let’s find the critical points of A2 : 0 = (A2 ...
... p A(x) = x 9 − x2 , that is the function we have to maximize, for x between 0 (the triangle collapses to a vertical line) and 3 (the triangle collapses to a horizontal line). As the hint suggests, we can just maximize A2 (x) = x2 (9 − x2 ) = 9x2 − x4 . Let’s find the critical points of A2 : 0 = (A2 ...
Direct Fitting
... Given the fitted function, we want to check for an adequate fit by plotting the data along with the fitted function. ...
... Given the fitted function, we want to check for an adequate fit by plotting the data along with the fitted function. ...
Privacy Preserving Data Mining: Additive Data Perturbation
... Evaluating loss of information • Indirect metric – Modeling quality • The accuracy of classifier, if used for classification modeling ...
... Evaluating loss of information • Indirect metric – Modeling quality • The accuracy of classifier, if used for classification modeling ...
Comparative Analysis of EM Clustering Algorithm and Density
... Clustering is organizing data into clusters or groups such that they have high intra-cluster similarity and low inter cluster similarity. The two clustering algorithms considered are EM and Density based algorithm. EM algorithm is general method of finding the maximum likelihood estimate of data dis ...
... Clustering is organizing data into clusters or groups such that they have high intra-cluster similarity and low inter cluster similarity. The two clustering algorithms considered are EM and Density based algorithm. EM algorithm is general method of finding the maximum likelihood estimate of data dis ...
Pseudocode Structure Diagrams
... Pseudocode is another method of how an algorithm can be written down. In pseudocode, the steps of the algorithm are written in simple English using some reserved words (key words). Pseudocode is usually used when the algorithm is too cumbersome to be displayed as a flowchart. The following reserved ...
... Pseudocode is another method of how an algorithm can be written down. In pseudocode, the steps of the algorithm are written in simple English using some reserved words (key words). Pseudocode is usually used when the algorithm is too cumbersome to be displayed as a flowchart. The following reserved ...
Basic principles of probability theory
... want to use these observations and estimate the parameters of the system. Once we know (it might be a challenging mathematical problem) how parameters and observations are related then we can use this to estimate the parameters. Maximum likelihood is one the techniques to estimate parameters using o ...
... want to use these observations and estimate the parameters of the system. Once we know (it might be a challenging mathematical problem) how parameters and observations are related then we can use this to estimate the parameters. Maximum likelihood is one the techniques to estimate parameters using o ...
IOSR Journal of Computer Engineering (IOSR-JCE)
... F. Angiulli et al. [8] proposed to find all subset of attributes examined outlying subset and outlying subspaces. It is very difficult to find subset because of exceptionsl growth. So its difficult to find outlying subspace too.So here using Outlying Subset Search Algorithm. For distance calculation ...
... F. Angiulli et al. [8] proposed to find all subset of attributes examined outlying subset and outlying subspaces. It is very difficult to find subset because of exceptionsl growth. So its difficult to find outlying subspace too.So here using Outlying Subset Search Algorithm. For distance calculation ...
Review of Kohonen-SOM and K-Means data mining Clustering
... same cell are similar, and the instances in different cells associate to each of them the class of all the individuals are different. In this point of view, SOM gives which are more similar to this code-vector than to the comparable results to state-of-the-art clustering algorithm others. But, in ca ...
... same cell are similar, and the instances in different cells associate to each of them the class of all the individuals are different. In this point of view, SOM gives which are more similar to this code-vector than to the comparable results to state-of-the-art clustering algorithm others. But, in ca ...
Automatically Building Special Purpose Search Engines with
... – Although it has no notion of scope, it also has an independence assumption about two independent views of data. ...
... – Although it has no notion of scope, it also has an independence assumption about two independent views of data. ...
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.