complex high-dimensional data
Hofner, Benjamin (2011): Boosting in structured additive models. Dissertation, LMU München: Fakultät für Mathematik, Informatik und Statistik
... in 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 derive ...
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Dimension Reduction and Classification for High Dimensional Complex Data.
... (EEG) data set ...EEG data is a 256 by 64 random ...the High Dimension, Low Sample Size (HDLSS) data and the presence of the matrix-valued predictors pose signicant challenges to the ...
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Classification Of High Dimensional Big Data In Distributed Computing Environment Using Fusion Based Learning
... Big data is large and complex unstructured data (images posted on Facebook, email, text messages, and GPS signals from mobile phones, tweets, and other updates on social media, ...Spatial ...
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Supporter in High Dimensional Data Classification
... assertively complex nonlinear issue into a course of action oflocally straight ones through neighbourhood learning, and after that learn incorporate relevance universally inside the broad edge ...a high ...
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Modelling Interactions in High-dimensional Data with Backtracking
... Our Backtracking algorithm has been presented in the context of the Lasso for the linear model. However, the real power of the idea is that it can be incorporated into any method that produces a path of increasingly ...
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Fast Data Collection for High Dimensional Data in Data Mining
... variables. For example a linear scaling of the input variables (that may be caused by a change of units for the measurements) is sufficient to modify the PCA results. Feature selection methods that are sufficient for ...
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A new approach for data visualization problem
... input data in the original high dimensional space and the output data in the projected ...original data and their corresponding inter point distances, but Sammon’s mapping is ...
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Association of repeatedly measured intermediate risk factors for complex diseases with high dimensional SNP data
... this data 3 individuals had a negative LDL cholesterol level and were therefore removed from the data, together with 27 individuals who had less than 2 observations for one or more of the 7 intermediate ...
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Data Mining Resolution on High Dimensional Data
... whole data representation and any reasoning process on the ...Big Data applications, where the key is to take the complex (nonlinear, many- to-many) data relationships, along with the evolving ...
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Machine learning techniques for high dimensional data
... If the images contain enough distinctive objectives that can be easily detected (i.e. the information carried by image local structures is more significant than that carried by the image intensity values), the feature ...
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RECURSIVE ANTIHUB2 OUTLIER DETECTION IN HIGH DIMENSIONAL DATA
... raw data collected from system. To identify unsupervised anomalies in high dimensional data is more ...in high dimensional data. Anomaly detection in high ...
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Cluster based boosting for high dimensional data
... In this paper, we discussed and explained various boosting problem and proposed solutions and also described some clustering techniques. Boosting proved advantageous for more accurate results in machine learning. Cluster ...
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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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Security Challenges Associated with High Dimensional Data
... Big Data analytics by using tools such as Kerberos, secure shell (SSH), and internet protocol security (IPsec) to get a handle on real-time ...Big Data infrastructure, and what's called ...
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Clustering of High-Dimensional Data Using Hubness
... over high dimensional data becomes difficult due to empty space phenomenon and concentration of ...of high dimensional data representation presents distance between data ...
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A Survey on High Dimensional Data Classification in Booster
... unique instance of fluffy demonstrating, in which the yield of framework is fresh and discrete. Fluffy demonstrating furnishes high interpretability and permits working with uncertain information. To investigate ...
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Clustering Algorithms for High Dimensional Data – A Survey
... exploratory data analysis which aims at summarizing main characteristics of ...in data sets and to identify a structure that might reside there, without having any specific background knowledge as ...
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Eigenvalue regularized covariance matrix estimators for high dimensional data
... the high-dimensional setting p/n → c > ...a data- splitting idea from Abadir et ...through data-splitting with theoretical justification of the split ...
175
Bayesian kernel projections for classification of high dimensional data
... eigenvalues, thus it is possible to evaluate the expected utility for all candidate models. The BKPC algorithm thus proceeds as follows: for each random split, the algo- rithm carries out a spectral decomposition of the ...
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High Performance Multidimensional Scaling for Large High Dimensional Data Visualization
... pubChem data, which is represented by a vector format, we also experimented on the proposed algorithm with other real data sets, which contains 30,000 biological se- quence data with respect to the ...
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