View/Download-PDF - International Journal of Computer Science
... “nearest neighbors" of the unknown sample.”Closeness" is defined in terms of Euclidean distance, The unknown sample is assigned the most common class among its k nearest neighbors. When k = 1, the unknown sample is assigned the class of the training sample that is closest to it in pattern space. 6) ...
... “nearest neighbors" of the unknown sample.”Closeness" is defined in terms of Euclidean distance, The unknown sample is assigned the most common class among its k nearest neighbors. When k = 1, the unknown sample is assigned the class of the training sample that is closest to it in pattern space. 6) ...
Enhanced Centroid-Based Classification Technique
... Since late 1990s, the explosive growth of Internet resulted in a huge quantity of documents available on-line. Technologies for efficient management of these documents are being developed continuously. One of representative tasks for efficient document management is text categorization, also called ...
... Since late 1990s, the explosive growth of Internet resulted in a huge quantity of documents available on-line. Technologies for efficient management of these documents are being developed continuously. One of representative tasks for efficient document management is text categorization, also called ...
Midterm 2 REVIEW 2 - Computer Science, Stony Brook University
... is a greedy algorithm that constructs decision trees in a top-‐down recursive divide-‐and-‐conquer manner • Tree STARTS as a single node represen*ng all training dataset (data table with records called samp ...
... is a greedy algorithm that constructs decision trees in a top-‐down recursive divide-‐and-‐conquer manner • Tree STARTS as a single node represen*ng all training dataset (data table with records called samp ...
attachment=1477
... 1.When u study the dwdm..study these topics and then move to some other topics wat u feel as important 2.most of the theory questions during the valuation they wil see correct definitions,key points,sub headings,presentation.... 3.Dont mugup all the points.. 4.write point by point 5.study the given ...
... 1.When u study the dwdm..study these topics and then move to some other topics wat u feel as important 2.most of the theory questions during the valuation they wil see correct definitions,key points,sub headings,presentation.... 3.Dont mugup all the points.. 4.write point by point 5.study the given ...
Short REVIEW for Midterm 2 - Computer Science, Stony Brook
... The recursive partitioning STOPS only when any one of the following conditions is TRUE 1. All records (samples) for the given node belong to the same class 2. There are no remaining attributes on which the samples (records in the data table) may be further partitioned Majority voting involves conver ...
... The recursive partitioning STOPS only when any one of the following conditions is TRUE 1. All records (samples) for the given node belong to the same class 2. There are no remaining attributes on which the samples (records in the data table) may be further partitioned Majority voting involves conver ...
Data Mining
... • Bayesian classifiers assume dependency between variables 8. Which of the following is a data mining task? • detect the kinds of DNA sensitive to a given drug ...
... • Bayesian classifiers assume dependency between variables 8. Which of the following is a data mining task? • detect the kinds of DNA sensitive to a given drug ...
A Comparative analysis on persuasive meta classification
... the performance of classifier Mi is so poor that its error exceeds 0.5, then we abandon it. Instead, we try again by generating a new Di training set, from which we derive a new Mi. The error rate of Mi affects how the weights of the training tuples are updated. If a tuple in round i was correctly c ...
... the performance of classifier Mi is so poor that its error exceeds 0.5, then we abandon it. Instead, we try again by generating a new Di training set, from which we derive a new Mi. The error rate of Mi affects how the weights of the training tuples are updated. If a tuple in round i was correctly c ...
slides
... • PCA tries to identify the components that characterise the data • ICA assumes that the data is no single entity, it is the linear combination of statistically independent sources, and tries to identify them • How is the independence measured? – Minimization of Mutual Information – Maximization of ...
... • PCA tries to identify the components that characterise the data • ICA assumes that the data is no single entity, it is the linear combination of statistically independent sources, and tries to identify them • How is the independence measured? – Minimization of Mutual Information – Maximization of ...
Performance Analysis of Classifiers to Effieciently Predict Genetic
... Data mining refers to the analysis of the large quantities of data that are stored in computers. Data mining is not specific to one type of media or data [1]. Data mining should be applicable to any kind of information repository. Data mining is being put into use and studied for databases, includin ...
... Data mining refers to the analysis of the large quantities of data that are stored in computers. Data mining is not specific to one type of media or data [1]. Data mining should be applicable to any kind of information repository. Data mining is being put into use and studied for databases, includin ...
Capturing Best Practice for Microarray Gene Expression Data Analysis
... •Cluster analysis, with goal of discovering natural classes •Leukemia data with 3 classes: ALL -> ALL-T and ALL-B •Same preprocessing as before, also normalize values for clustering •Used two clustering methods in Clementine package, both able to discover natural classes in data, to the authors’ sat ...
... •Cluster analysis, with goal of discovering natural classes •Leukemia data with 3 classes: ALL -> ALL-T and ALL-B •Same preprocessing as before, also normalize values for clustering •Used two clustering methods in Clementine package, both able to discover natural classes in data, to the authors’ sat ...
CLASSIFICATION
... In this classification technique, the training set includes classes. Examine K near items to be classified. New items placed in class with most number of close items. An object is classified by a majority vote of its neighbors, with the object being assigned the class most common amongst its k neare ...
... In this classification technique, the training set includes classes. Examine K near items to be classified. New items placed in class with most number of close items. An object is classified by a majority vote of its neighbors, with the object being assigned the class most common amongst its k neare ...
Image Classification - UNE Faculty/Staff Index Page
... – The means of the information classes derived from the training sets. • The pixel is designated to the class with the shortest distance. • Some versions of this classifier use the standard deviation of the classes to determine a minimum distance threshold. ...
... – The means of the information classes derived from the training sets. • The pixel is designated to the class with the shortest distance. • Some versions of this classifier use the standard deviation of the classes to determine a minimum distance threshold. ...
Karishma Agrawal
... § Conducted classification of water pumps in different categories, prioritizing identification of pumps that need repair. § Dealt with skewed class distribution and features with extremely high arity. § Used Random Forest for final model with appropriate operating point to minimize false positive ...
... § Conducted classification of water pumps in different categories, prioritizing identification of pumps that need repair. § Dealt with skewed class distribution and features with extremely high arity. § Used Random Forest for final model with appropriate operating point to minimize false positive ...
![[slides] Kernels and clustering](http://s1.studyres.com/store/data/004208335_1-990c8ce2efe99730694ed35e841f416e-300x300.png)
CS395T/CAM395T (Spring 2008) Data Mining: A Statistical Learning
... in massive data sets. This graduate course will focus on various mathematical and statistical aspects of data mining. Topics covered include supervised learning (regression, classification, support vector machines) and unsupervised learning (clustering, principal components analysis, dimensionality ...
... in massive data sets. This graduate course will focus on various mathematical and statistical aspects of data mining. Topics covered include supervised learning (regression, classification, support vector machines) and unsupervised learning (clustering, principal components analysis, dimensionality ...
Chapter 10 – Discriminant Analysis
... When this condition is met, DA is more efficient than other methods (i.e. needs less data to obtain similar accuracy) Even when it is not met, DA is robust when we have enough cases in smallest class (> 20) . This means it can be used with dummy variables! 2. Assumes correlation among predictors ...
... When this condition is met, DA is more efficient than other methods (i.e. needs less data to obtain similar accuracy) Even when it is not met, DA is robust when we have enough cases in smallest class (> 20) . This means it can be used with dummy variables! 2. Assumes correlation among predictors ...
Paper D1.S3.8 - Department of Computer and Information Sciences
... 1. The tree is built by selecting one attribute at a time - the one that ‘best’ separates the classes. 2. The set of examples is then partitioned according to value of selected attributes. ...
... 1. The tree is built by selecting one attribute at a time - the one that ‘best’ separates the classes. 2. The set of examples is then partitioned according to value of selected attributes. ...
K-nearest neighbors algorithm
In pattern recognition, the k-Nearest Neighbors algorithm (or k-NN for short) is a non-parametric method used for classification and regression. In both cases, the input consists of the k closest training examples in the feature space. The output depends on whether k-NN is used for classification or regression: In k-NN classification, the output is a class membership. An object is classified by a majority vote of its neighbors, with the object being assigned to the class most common among its k nearest neighbors (k is a positive integer, typically small). If k = 1, then the object is simply assigned to the class of that single nearest neighbor. In k-NN regression, the output is the property value for the object. This value is the average of the values of its k nearest neighbors.k-NN is a type of instance-based learning, or lazy learning, where the function is only approximated locally and all computation is deferred until classification. The k-NN algorithm is among the simplest of all machine learning algorithms.Both for classification and regression, it can be useful to assign weight to the contributions of the neighbors, so that the nearer neighbors contribute more to the average than the more distant ones. For example, a common weighting scheme consists in giving each neighbor a weight of 1/d, where d is the distance to the neighbor.The neighbors are taken from a set of objects for which the class (for k-NN classification) or the object property value (for k-NN regression) is known. This can be thought of as the training set for the algorithm, though no explicit training step is required.A shortcoming of the k-NN algorithm is that it is sensitive to the local structure of the data. The algorithm has nothing to do with and is not to be confused with k-means, another popular machine learning technique.