Except for linear regression and ridge-regression, are there are algorithms (with examples) in DAAL which could be used for regression of an arbitrary function without an explicit model? What about time-series forecasting?
The present version of the library supports linear and ridge regression only. Can you provide additional details related to your first question - specific algorithm(s) you keep in mind, typical input problem sizes, precision used to represent the input data, the need in online and/or distributed mode of the computations?
For the time series topic - indeed, we analyze support of the functionality in the library, and your inputs similar to the ones above on the specific algorithms/use cases would be helpful as well.
Within AI and machine learning the algorithms are rarely very generic. They mostly apply to some specific type of problem and work well with that. It would be great, if you could include at least one real world example per algorithm rather than random generated numbers. And add short description of the problem (types) being solved.
Especially in the case of Neural Networks it would help a lot to have a prebuilt layer setups, which only need to be modified for feature count and type for example, but could be used directly. Depending on the area of application there could be hundreds of different configurations. Having ready-to-use-configs with real life data would make a big difference. Examples for NN:
Multi-class classifications with ordinal, categorical and real valued inputs from tables.
Handling of missing values with NNs
Regression example (real valued output with all three possible input feature types an real valued output )
At least three photo classification examples.
Time series prediction.
Thanks, Atmapuri, for the additional details. We will analyze what can be done to address your feedback. Andrey