ÇUKUROVA UNIVERSITY INSTITUTE OF NATURAL AND APPLIED
... classification method using the feature and observation space information. With this method they had performed a fine classification when a pair of the spatial coordinate of the observation data in the observation space and its corresponding feature vector in the feature space is provided (Kubota et ...
... classification method using the feature and observation space information. With this method they had performed a fine classification when a pair of the spatial coordinate of the observation data in the observation space and its corresponding feature vector in the feature space is provided (Kubota et ...
Comparison of Hierarchical and Non
... approach is called as bottom to top approach. In this approach, data points set clusters as combining with each other [8]. Divisive approach has contrary process and is called top to bottom. Required calculation load by divisive and agglomerative approaches are similar [9]. Clustering algorithms in ...
... approach is called as bottom to top approach. In this approach, data points set clusters as combining with each other [8]. Divisive approach has contrary process and is called top to bottom. Required calculation load by divisive and agglomerative approaches are similar [9]. Clustering algorithms in ...
Scalable Sequential Spectral Clustering
... As discussed above, the construction of the graph W takes quadratic space and time because of the computation of pairwise distance of data points. This process is easy to sequentialize. Specifically, we can keep only one sample of data xi in the memory and then load all the other data from the disk s ...
... As discussed above, the construction of the graph W takes quadratic space and time because of the computation of pairwise distance of data points. This process is easy to sequentialize. Specifically, we can keep only one sample of data xi in the memory and then load all the other data from the disk s ...
Data Analysis
... divided into training and test sets, with training set used to build the model and test set used Data Analysis to validate it. ...
... divided into training and test sets, with training set used to build the model and test set used Data Analysis to validate it. ...
A New Ensemble Model based Support Vector Machine for
... Normally, confusion matrix is used to estimate the accuracy. A confusion matrix is a matrix that shows the relationships between true class and predicted class. The below figure shows a confusion matrix. Additionally, 5-fold cross validation is presented in order to reduce the variance of the result ...
... Normally, confusion matrix is used to estimate the accuracy. A confusion matrix is a matrix that shows the relationships between true class and predicted class. The below figure shows a confusion matrix. Additionally, 5-fold cross validation is presented in order to reduce the variance of the result ...
Network Intrusion Detection Using a Hardware
... algorithm is a famous technique used in machine learning. It does not require optimization and is easy to implement in both software and hardware. The KNN algorithm stores all training samples in its knowledgebase. It then compares a new test sample to all learned samples or neurons in its knowledge ...
... algorithm is a famous technique used in machine learning. It does not require optimization and is easy to implement in both software and hardware. The KNN algorithm stores all training samples in its knowledgebase. It then compares a new test sample to all learned samples or neurons in its knowledge ...
Old Exam Questions
... two variables X and Y is not affected by the change of unit of measurments of X or Y. (consider linear transformations such as measuring temperature by oC or oF:X’=aX+b,Y’=cY+d ) Consider the regression of Y on X, Y = + X, show that the least square estimates of and are affected from the unit of mea ...
... two variables X and Y is not affected by the change of unit of measurments of X or Y. (consider linear transformations such as measuring temperature by oC or oF:X’=aX+b,Y’=cY+d ) Consider the regression of Y on X, Y = + X, show that the least square estimates of and are affected from the unit of mea ...
Trajectory Boundary Modeling of Time Series for Anomaly Detection
... generalize when given more than one training series. By online, we mean that each test point receives an anomaly score, with an upper bound on computation time. We accept that there is no "best" anomaly detection algorithm for all data, and that many algorithms have ad-hoc parameters which are tuned ...
... generalize when given more than one training series. By online, we mean that each test point receives an anomaly score, with an upper bound on computation time. We accept that there is no "best" anomaly detection algorithm for all data, and that many algorithms have ad-hoc parameters which are tuned ...
Literature Survey on Outlier Detection Techniques For Imperfect
... Index Copernicus Value (2013): 6.14 | Impact Factor (2013): 4.438 neighborhood of various regions, and plotting a smoothed version of the curve 2) classification of uncertain data A closely related problem is that of classification of uncertain data in which the aim is to classify a test instance in ...
... Index Copernicus Value (2013): 6.14 | Impact Factor (2013): 4.438 neighborhood of various regions, and plotting a smoothed version of the curve 2) classification of uncertain data A closely related problem is that of classification of uncertain data in which the aim is to classify a test instance in ...
Multi-Document Content Summary Generated via Data Merging Scheme
... algorithm that are used for preprocess the documents to get clean documents. The weighting methods has provided a solution to decrease the negative effect of the words, almost all document clustering algorithms including algorithm prefer to consider these words for new stop words, and ignore them in ...
... algorithm that are used for preprocess the documents to get clean documents. The weighting methods has provided a solution to decrease the negative effect of the words, almost all document clustering algorithms including algorithm prefer to consider these words for new stop words, and ignore them in ...
A Novel Algorithm for Privacy Preserving Distributed Data Mining
... demantant sent signed and encrypted message to the CA. This message contains a demandant ID and mining request and also ID of each data server. In the second step CA server generated key encrypt and decrypt but only sends encryption key to the data servers. In the third step, data servers, encrypted ...
... demantant sent signed and encrypted message to the CA. This message contains a demandant ID and mining request and also ID of each data server. In the second step CA server generated key encrypt and decrypt but only sends encryption key to the data servers. In the third step, data servers, encrypted ...
Finding the statistical test necessary for your scientific research
... NOTE: This presentation has the main purpose to assist researchers and students in choosing the appropriate statistical test for studies that examine one variable (Univariate). Some multivariates analyses are also included. Please proceed to the next page ... If you have any suggestion, criticism, p ...
... NOTE: This presentation has the main purpose to assist researchers and students in choosing the appropriate statistical test for studies that examine one variable (Univariate). Some multivariates analyses are also included. Please proceed to the next page ... If you have any suggestion, criticism, p ...
Distributed algorithm for privacy preserving data mining
... main servers. This group of algorithms is considered as the first represented algorithms in the field of privacy, their method of action is in a way that first the main servers make changes in data servers, for example, by adding noise, coding and so on, and then these data are transferred to mining ...
... main servers. This group of algorithms is considered as the first represented algorithms in the field of privacy, their method of action is in a way that first the main servers make changes in data servers, for example, by adding noise, coding and so on, and then these data are transferred to mining ...
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.