Data sources define interfaces for access and management of data in raw format and out-of-memory data. A data source is closely coupled with the data dictionary that describes the structure of the data associated with the data source. To create the associated data dictionary, you can do one of the following:
To get the best overall performance when computing low order moments:
The correlation distance matrix algorithm accepts the input described below. Pass the Input ID as a parameter to the methods that provide input for your algorithm. For more details, see Algorithms.
To get the best overall performance of the PCA algorithm:
Training and prediction algorithms in Intel® Data Analytics Acceleration Library (Intel® DAAL) include a range of popular machine learning algorithms. Unlike analysis algorithms, which are intended to characterize the structure of data sets, machine learning algorithms model the data. Modeling operates in two major stages:
Boosting is a set of algorithms intended to build a strong classifier from an ensemble of weighted weak classifiers by iterative re-weighting according to some accuracy measure for weak classifiers. A weak learner is a classifier that has only slightly better performance than random guessing. Weak learners are usually very simple and fast, and they focus on classification of very specific features.
C++ API Reference for Intel(R) Data Analytics Acceleration Library