Sentence examples for learning repositories from inspiring English sources

The performance of the proposed classifier named as SAEDE-RBFN has been extensively evaluated on seven datasets retrieved from University of California, Irvine (UCI) and KEEL machine learning repositories after imputation by mean, nearest neighbor, and proposed method.

These datasets were collected from the the KEEL [21] and UCI [22] machine learning repositories and represent a variety of complexity, number of classes, number of attributes, number of instances, and imbalance ratio (ratio of the size of the majority class to the minority class).

The metadata generated in (4) dealing with related domain ontology concepts, and the system-centric metadata generated in (5) are then used to discover and filter out learning resources from various learning repositories.

Additionally, the framework has been evaluated against the open-source Dodgers dataset, available at the UCI machine learning repository, and against the R statistical toolbox.

Experimental results on UCI machine learning repository data sets reveal that the computational time for summarizing large text collections is drastically reduced using the MapReduce framework and MapReduce provides scalability for accommodating large text collections for summarizing.

Comparing the HW/SW EEFTI implementation with the pure software implementation suggests that the proposed HW/SW architecture offers substantial speedups for all the tests performed on the selected UCI (University of California, Irvine) machine learning repository datasets.

The comparison of the HW/SW EFTI implementation with the pure software implementation suggests that the proposed HW/SW architecture offers substantial DT induction time speedups for the selected benchmark datasets from the standard UCI machine learning repository database.

The performances of our method have been extensively tested on the UCI machine learning repository, as well as a real clinical problem, i.e., tissue classification in prostate ultrasound images.

The performance of the PBL-McRBFN is evaluated using a set of benchmark classification problems from UCI machine learning repository and two practical problems, viz., the acoustic emission signal classification and the mammogram for cancer classification.

The numerical experiments in this study compare the classification accuracy of subspace ECOC, classical ECOC, one-versus-one, and one-versus-all methods over a set of UCI machine learning repository datasets and two image vision applications.

Therefore, we set an effective way to correct the saturation problem in the learning process, which can be denoted as the pseudo code in Algorithm 3. In this section, we evaluate the performance of the ILRBF-BP algorithm using two artificial classification problems from [33] and three classification problems from the UCI machine learning repository [34].