US 11,836,646 B2
Efficiently constructing regression models for selectivity estimation
Anshuman Dutt, Sammamish, WA (US); Chi Wang, Redmond, WA (US); Vivek Ravindranath Narasayya, Redmond, WA (US); and Surajit Chaudhuri, Kirkland, WA (US)
Assigned to MICROSOFT TECHNOLOGY LICENSING, LLC, Redmond, UT (US)
Filed by Microsoft Technology Licensing, LLC, Redmond, WA (US)
Filed on Jun. 30, 2020, as Appl. No. 16/917,857.
Prior Publication US 2021/0406744 A1, Dec. 30, 2021
Int. Cl. G06N 7/00 (2023.01); G06N 7/01 (2023.01); G06N 20/00 (2019.01); G06F 18/214 (2023.01); G06F 18/21 (2023.01)
CPC G06N 7/01 (2023.01) [G06F 18/214 (2023.01); G06F 18/217 (2023.01); G06N 20/00 (2019.01)] 20 Claims
OG exemplary drawing
 
1. A system comprising:
one or more processors;
memory in electronic communication with the one or more processors; and
instructions stored in the memory, the instructions being executable by the one or more processors to:
train a model on a set of labeled training examples using cross-validation, the set of labeled training examples having a size;
determine that the model does not satisfy an accuracy target;
determine a geometric step size for increasing the size of the set of labeled training examples;
increase the size of the set of labeled training examples to a new size based on the geometric step size;
generate labels for the additional training examples needed to increase the size of the set of labeled training examples to the new size;
add the additional training examples and the generated labels to the set of labeled training examples; and
train the model on the set of labeled training examples using cross-validation.