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The discussion is illustrated by tests on databases from the UCI machine learning database repository.
Several results using the UCI repository of Machine learning database are showed for classification and approximation tasks.
We used three datasets, two of which are from the UCI machine learning database: (1) Wisconsin Breast Cancer (WBC) and (2) US Census dataset (Adult).
Here we have considered two data sets from UCI machine learning database having nine and ten dimensional feature spaces for classification.
Proposed method has been validated using the UCI machine learning database and further applied to a couple of real-world pattern recognition problems (the face recognition and gene expression based cancer classification).
Experiential results on five popular benchmark databases taken from University of California Irvine (UCI) machine learning database show high classification accuracy that is competitive with other classical and mainstream classifiers.
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Another spambase dataset, obtained from UCI repository of machine learning databases, is also used to evaluate the performance of the proposed ABBDT approach.
To illustrate the performance of LS, CS, CLS, and the proposed CCLS algorithm, we compare these four algorithms on several high-dimensional machine learning databases [26] including Ionoshpere, Image Segmentation, Soybean, and Vehicle datasets.
Scale is the name of the game for these services, both as a lever for expanding a company's bigger business (in Amazon's case: e-commerce), but also as a way of amassing ever more data into its machine-learning database to make it work better.
This prediction model is constructed with data mining methods at the intersection of statistics, machine learning and database system.
In smart sustainable cities, big data analytics should involve highly sophisticated and dedicated techniques and algorithms (data mining, machine learning, statistics, database query, etc).
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