high-dimensional data sets
1. Optimal feature selection algorithm for high dimensional data sets using particle swarm optimization
... Abstract: In high dimensional data sets, there are large numbers of features in classification problems. But all of them are not necessarily beneficial for classification. Feature selection ...
12
Improving Efficiency In High Dimensional Data Sets
... in high dimensional information with few perceptions are ending up more typical, particularly in microarray ...with high dimensional ...
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Detecting Outliers in High Dimensional Data Sets using Z Score Methodology
... in data that do not conform to estimated ...in high-dimensional ...distant data based on data positions on ...with high dimensional ...
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A Review On: Finding Outlier Points On Real Dimensional Data Sets
... of high dimensional data mining, information retrieval to finding outlier point in multi dimensional data ensemble subspace clustering, spam detection, improved k-means algorithm based ...
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Hofner, Benjamin (2011): Boosting in structured additive models. Dissertation, LMU München: Fakultät für Mathematik, Informatik und Statistik
... complex, high-dimensional data sets that are hard to handle by using classical methods such as linear models with stepwise variable ...small data sets, and they can be used to ...
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Computational steering of a multi-objective genetic algorithm using a PDA
... of high dimensional data ...of high dimensional patterns can be complicated by the disconnected representation of multiple aspects of the same point in high dimensional ...
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A Novel Collective Neighbor Clustering in High Dimensional Data
... all high-dimensional data sets tend to be sparse, because the number of points required to represent any distribution grows exponentially with the number of ...for high- ...
5
Hadoop neural network for parallel and distributed feature selection
... and high dimensional data sets that cannot be processed on standard ...on data sets that require at a minimum multiple CPUs but more likely multiple compute nodes to ...to ...
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Feature Subset Selection using Rough Sets for High Dimensional Data
... Cluster analysis divides data into meaningful or useful groups (clusters). If meaningful clusters are the goal, then the resulting clusters should capture the “natural” structure of the data. Cluster ...
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Improved k-means clustering using principal component analysis and imputation methods for breast cancer dataset
... partition data points into disjoint group such that data point belonging to same cluster are similar while data point that belong to different clusters is ...of high dimensional ...
26
The BACON Approach for Rank-Deficient Data
... Rank-deficient data are not uncommon in ...and/or high- dimensional ...multivariate data is extended here to include rank-deficient ...deficient data based on the original BACON ...the ...
21
Data Mining Resolution on High Dimensional Data
... distributed data are the critical goals for Big Data processing to change from “quantity” to ...Big Data processing mainly depends on parallel programming models like MapReduce, as well as providing ...
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Pyro: Deep Universal Probabilistic Programming
... large data sets and high-dimensional models, Pyro uses stochastic variational inference algorithms and probability distributions built on top of PyTorch, a modern GPU-accelerated deep learning ...
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Booster in High Dimensional Data Classification
... This paper proposes Q-statistic to evaluate the performance of an FS algorithm with a classifier. This is a hybrid measure of the prediction accuracy of the classifier and the stability of the selected features. Then the ...
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A permutation test for comparing rotational symmetry in three-dimensional rotation data sets
... the data are less spread ...simulated data sets are less alike, making the test more ...the data become less spread, the test becomes more ...small data sets exhibiting a ...
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Feature Selection for Small Sample Sets with High Dimensional Data Using Heuristic Hybrid Approach
... analyzing high dimensional data, especially with a small number of ...sample sets and high dimensional data, exploring a large search space and learning from insufficient ...
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Singular Value Decomposition for High Dimensional Data
... the data matrix, measure the differences between the fitted values and the original values for those entries, and choose the threshold levels that minimize the ...out sets of rows and columns and choosing ...
106
RECURSIVE ANTIHUB2 OUTLIER DETECTION IN HIGH DIMENSIONAL DATA
... in high-dimensional data identifies seven issues in addition to distance concentration: noisy attributes definition of reference sets, bias (comparability) of scores, interpretation and ...
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Training and Testing Low-degree Polynomial Data Mappings via Linear SVM
... forming data to a high dimensional space, but training and testing large data sets is often time ...larger data sets using linear SVM without ...mapped data and ...
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A new approach for data visualization problem
... multidimensional data making use of humans’ natural visual ...specifically, data visualization reveals relationships in data sets that are not evident from the raw data, by using ...
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