Learning device and learning method

A learning device for learning of a decision tree, the learning device comprising:

• a controller, and
• learning circuitry configured to:
• calculate a branch score used for determining a branch condition for a node of the decision tree based on a cumulative sum of gradient information corresponding to each value of a feature amount of learning data;
• and
• perform scaling on a value related to the cumulative sum used for calculating the branch score by the learning circuitry to fall within a numerical range with which the branch score is capable of being calculated,
• wherein the learning circuitry is configured by a field-programmable gate array, and the controller performs data communication with the field-programmable gate array,
• the learning circuitry is further configured to perform an operation on respective histograms of all feature amounts input at a time and store the histograms, and perform an operation on all of the feature amounts in parallel, and
• the learning circuitry is configured to calculate Gain as the branch score by the following expression (1):
• G
• ⁢
• ain
• =
• 1
• 2
• ⁡
• [
• G
• L
• 2
• H
• L
• +
• λ
• +
• G
• R
• 2
• H
• R
• +
• λ
• -
• (
• G
• L
• +
• G
• R
• )
• 2
• H
• L
• +
• H
• R
• +
• λ
• ]
• -
• γ
• wherein:
• Gain:
• the branch score,
• GL:
• a sum of gradient information (first order gradients) of samples branched to a left node,
• GR:
• a sum of gradient information (first order gradients) of samples branched to a right node,
• HL:
• a sum of gradient information (second order gradients) of samples branched to the left node,
• HR:
• a sum of gradient information (second order gradients) of samples branched to the right node, and
• γ,λ:
• regularization terms.