- Journal of AI and Data Mining
... properties, one of which is distance. Distance is a numerical description of how much objects are departed. In the Euclidean space n, the distance between two points is usually given by the Euclidean distance (2-norm distance). Based on other norms, different distances are used such as 1-, p- and in ...
... properties, one of which is distance. Distance is a numerical description of how much objects are departed. In the Euclidean space n, the distance between two points is usually given by the Euclidean distance (2-norm distance). Based on other norms, different distances are used such as 1-, p- and in ...
Clustering Methods
... Optimal dividing point pseudo code of Step 2 Step 2.1: Calculate projections on the principal axis. Step 2.2: Sort vectors according to the projection. Step 2.3: FOR each vector xi DO: - Divide using xi as dividing point. - Calculate distortion of subsets D1 and D2. ...
... Optimal dividing point pseudo code of Step 2 Step 2.1: Calculate projections on the principal axis. Step 2.2: Sort vectors according to the projection. Step 2.3: FOR each vector xi DO: - Divide using xi as dividing point. - Calculate distortion of subsets D1 and D2. ...
Evolving Neural Networks using Ant Colony Optimization with
... at work without heuristic bias ACO leads in very poor results”. The use of heuristic information is very important for ACO and has a great impact to the overall performance of the algorithm. For example, at the initial stage of training an ANN, the pheromone trails in all tables assigned to each con ...
... at work without heuristic bias ACO leads in very poor results”. The use of heuristic information is very important for ACO and has a great impact to the overall performance of the algorithm. For example, at the initial stage of training an ANN, the pheromone trails in all tables assigned to each con ...
An Introduction to Regression Analysis
... in a two-dimensional diagram—with two explanatory variables we need three dimensions, and instead of estimating a line we are estimating a plane. Multiple regression analysis will select a plane so that the sum of squared errors—the error here being the vertical distance between the actual value of ...
... in a two-dimensional diagram—with two explanatory variables we need three dimensions, and instead of estimating a line we are estimating a plane. Multiple regression analysis will select a plane so that the sum of squared errors—the error here being the vertical distance between the actual value of ...
Particle Swarm Optimisation for Outlier Detection
... directly related to the value of M inP tn. The idea of LOF has been extended and improved in different ways. For example, the Connectivity-based Outlier Factor (COF) scheme [20] extends the LOF algorithm for detecting outliers in data patterns that are difficult to discover using LOF. The LSC-Mine [ ...
... directly related to the value of M inP tn. The idea of LOF has been extended and improved in different ways. For example, the Connectivity-based Outlier Factor (COF) scheme [20] extends the LOF algorithm for detecting outliers in data patterns that are difficult to discover using LOF. The LSC-Mine [ ...
Segmentation using probabilistic model
... Figure from “Representing Images with layers,”, by J. Wang and E.H. Adelson, IEEE Transactions on Image Processing, 1994, c 1994, IEEE Computer Vision - A Modern Approach Set: Probability in segmentation Slides by D.A. Forsyth ...
... Figure from “Representing Images with layers,”, by J. Wang and E.H. Adelson, IEEE Transactions on Image Processing, 1994, c 1994, IEEE Computer Vision - A Modern Approach Set: Probability in segmentation Slides by D.A. Forsyth ...
Enhanced form of solving real coded numerical optimization
... In this paper the parameter setting are as colony size= 20, 40, 80, Maximum number of cycles (MCN) = 100, 2000, 3000 respectively for the dimension 10. Food sources equal the half of the colony size. The percentage of onlooker bees was 50% of the colony, the employed bees were 50% of the colony and ...
... In this paper the parameter setting are as colony size= 20, 40, 80, Maximum number of cycles (MCN) = 100, 2000, 3000 respectively for the dimension 10. Food sources equal the half of the colony size. The percentage of onlooker bees was 50% of the colony, the employed bees were 50% of the colony and ...
Basics of machine learning, supervised and unsupervised learning
... Overfitting and generalization • Overfitting: the model describes random noise or errors instead of the underlying relationship. • Frequently occurs when model is overly complex (e.g. has too many parameters) relative to the number of observations. • Has poor predictive performance. ...
... Overfitting and generalization • Overfitting: the model describes random noise or errors instead of the underlying relationship. • Frequently occurs when model is overly complex (e.g. has too many parameters) relative to the number of observations. • Has poor predictive performance. ...
![[pdf]](http://s1.studyres.com/store/data/018886435_1-3fd37170dad6eb4e18443c89e6f4de8b-300x300.png)
A Rule-Based Classification Algorithm for Uncertain Data
... missing attribute values. However, the problem studied in this paper is different from before. Instead of assuming part of the data has missing or noisy values, we allow the whole dataset to be uncertain. Furthermore, the uncertainty is not shown as missing or erroneous values but represented as unc ...
... missing attribute values. However, the problem studied in this paper is different from before. Instead of assuming part of the data has missing or noisy values, we allow the whole dataset to be uncertain. Furthermore, the uncertainty is not shown as missing or erroneous values but represented as unc ...
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.