Patent application EP19162068A (EP), filed 2019-03-12
Publication number: EP3540654A1
Title: LEARNING CLASSIFICATION DEVICE AND LEARNING CLASSIFICATION METHOD
Abstract: [0001]    A learning classification device (1) includes a data memory (30), a learning unit (20), and a classification unit (50). The data memory (30) is configured to store training data for learning a decision tree. The learning unit (20) is configured to read a plurality of feature quantities included in the training data from the data memory (30) by single access and derive data of a node based on the plurality of feature quantities, to learn the decision tree. The classification unit (50) is configured to determine where the training data read from the data memory is to be split to from the node, based on the data of the node derived by the learning unit (20).
IPC codes: G06N   5/    00            A I
G06F  12/    02            A I
G06F  12/    06            A I
G06N  20/    20            A I
G06N   5/    01            A I
Claims: number: 1
text: A learning classification device comprising:
number: 2
text: The learning classification device according to claim 1, wherein
the learning unit
number: 3
text: The learning classification device according to claim 2, wherein
the learning unit includes:
gain calculation units corresponding to the number of the memories, and configured to calculate split scores for thresholds, based on the histograms of the feature quantities stored in the memories; and
a deriving unit configured to derive a number of a feature quantity and a threshold with which a split score is optimum, as the data of the node.
number: 4
text: The learning classification device according to any one of claims 1 to 3, wherein the classification unit is configured to read label information of the training data together with the feature quantities of the training data from the data memory.
number: 5
text: The learning classification device according to any one of claims 1 to 4, wherein the learning unit is configured to learn a next decision tree by gradient boosting, based on a learning result of the learned decision tree.
number: 6
text: The learning classification device according to claim 5, wherein the classification unit is configured to calculate a first-order gradient and a second-order gradient corresponding to an error function of each training data, and a leaf weight for the training data, and writes the calculated first-order gradient, second-order gradient, and leaf weight into the data memory in order that a next decision tree is learned by the gradient boosting by the learning unit.
number: 7
text: The learning classification device according to any one of claims 1 to 6, wherein
the data memory includes at least two bank regions for storing addresses of the training data,
the at least two bank regions are switched between a bank region for reading and a bank region for writing every time a level of a node to be learned is switched,
the learning unit is configured to read addresses of training data split at a node from the bank region for reading and read the training data from regions of the data memory indicated by the addresses, and
the classification unit writes addresses of the training data split at the node into the bank region for writing.
number: 8
text: The learning classification device according to claim 7, wherein
the classification unit is configured to
number: 9
text: The learning classification device according to any one of claims 1 to 8, wherein the classification unit is configured to perform classification operation for classification data in classification while sharing a configuration for performing classification operation for the training data in learning.
number: 10
text: The learning classification device according to any one of claims 1 to 9, further comprising a model memory configured to store data of a node of the decision tree, wherein
the learning unit is configured to
number: 11
text: The learning classification device according to claim 10, wherein
the data memory is configured to store classification data to be classified using the decision tree,
the model memory includes independent ports for reading data of nodes at once for respective levels of the decision tree, and
the classification unit is configured to perform splitting processes for the respective levels as a pipeline process for the classification data read from the data memory, based on data of nodes at the levels simultaneously read from the ports of the model memory.
number: 12
text: The learning classification device according to any one of claims 1 to 11, wherein the learning unit performs learning using part of the feature quantities among all the feature quantities of the training data.
number: 13
text: The learning classification device according to any one of claims 1 to 12, wherein at least the data memory, the learning unit, and the classification unit are configured on a field-programmable gate array (FPGA).
number: 14
text: The learning classification device according to any one of claims 1 to 13, wherein the learning unit performs learning using part of the training data among all the training data.
number: 15
text: A learning classification method comprising:
Office action: search_report, dated 2019-09-18
Prior art cited: ref_id: JPS5032602B1
ref_type: patent
severity: background
ref_id: WO2016090290A1
ref_type: patent
severity: background
ref_id: - QI MENG ET AL: "A Communication-Efficient Parallel Algorithm for Decision Tree", ARXIV.ORG, CORNELL UNIVERSITY LIBRARY, 201 OLIN LIBRARY CORNELL UNIVERSITY ITHACA, NY 14853, 4 November 2016 (2016-11-04), XP080729340
ref_type: npl
severity: background
ref_id: - THOMAS KECK: "FastBDT: A speed-optimized and cache-friendly implementation of stochastic gradient-boosted decision trees for multivariate classification", ARXIV.ORG, CORNELL UNIVERSITY LIBRARY, 201 OLIN LIBRARY CORNELL UNIVERSITY ITHACA, NY 14853, 20 September 2016 (2016-09-20), XP080728108
ref_type: npl
severity: background
ref_id: WO2016090290
ref_type: patent
severity: background
ref_id: - QI MENG ET AL, "A Communication-Efficient Parallel Algorithm for Decision Tree", ARXIV.ORG, CORNELL UNIVERSITY LIBRARY, 201 OLIN LIBRARY CORNELL UNIVERSITY ITHACA, NY 14853, (20161104), XP080729340 [I] 1-15 * the whole document *
ref_type: npl
severity: background
ref_id: - THOMAS KECK, "FastBDT: A speed-optimized and cache-friendly implementation of stochastic gradient-boosted decision trees for multivariate classification", ARXIV.ORG, CORNELL UNIVERSITY LIBRARY, 201 OLIN LIBRARY CORNELL UNIVERSITY ITHACA, NY 14853, (20160920), XP080728108 [I] 1-15 * the whole document *
ref_type: npl
severity: background
Final disposition: pending (2019-09-18)