Patent application EP17000986A (EP), filed 2017-06-09
Publication number: EP3258425A1
Title: INFORMATION PROCESSING APPARATUS, INFORMATION PROCESSING METHOD, AND STORAGE MEDIUM
Abstract: On the basis of a difference between first distribution regarding target task learning data as a plurality of learning data which belongs to a first category of a first task as a target task and second distribution regarding a plurality of learning data which belongs to the first category of source task learning data as learning data which belongs to the first category of a second task different from the first task, a transformation parameter for transforming the source task learning data is adjusted, the source task learning data is transformed based on the adjusted transformation parameter, and a classifier regarding the first task is generated based on the transformed source task learning data and the target task learning data.
IPC codes: G06N  20/    10            A I
G06F  16/   903            A I
G06N  20/    00            A I
Claims: number: 1
text: An information processing apparatus comprising:
number: 2
text: The information processing apparatus according to Claim 1, wherein the adjusting unit adjusts the transformation parameter so that density distribution of the first distribution and density distribution of the second distribution are close.
number: 3
text: The information processing apparatus according to Claim 2, wherein the adjusting unit adjusts the transformation parameter so as to locally minimize an estimation value of an L2 distance between the first distribution and the second distribution.
number: 4
text: The information processing apparatus according to Claim 2, wherein the adjusting unit adjusts the transformation parameter so as to locally minimize an estimation value of a relative Piason distance between the first distribution and the second distribution.
number: 5
text: The information processing apparatus according to any one of Claims 2 to 4, wherein the adjusting unit adjusts the transformation parameter so as to locally minimize a difference between the first distribution and the second distribution by using a gradation method.
number: 6
text: The information processing apparatus according to any one of Claims 1 to 5, further comprising a searching unit configured to search for the second task as a task similar to the first task, and
wherein, the second obtaining unit obtains the learning data of the second task, as the source task learning data, searched for by the searching unit.
number: 7
text: The information processing apparatus according to any one of Claims 1 to 6, wherein:
number: 8
text: The information processing apparatus according to any one of Claims 1 to 6, wherein the generating unit selects a feature amount for use in the generation of the classifier on the basis of the target task learning data and the source task learning data transformed by the transforming unit, and generates the classifier on the basis of the selected feature amount.
number: 9
text: The information processing apparatus according to any one of Claims 1 to 8, further comprising a classifying unit configured to classify whether or not input data as a target of the classification belongs to the first category by using the classifier generated by the generating unit.
number: 10
text: An information processing method which is executed by an information processing apparatus, the method comprising:
number: 11
text: A non-transitory computer-readable storage medium of storing a program for causing a computer to execute:
Office action: search_report, dated 2017-12-20
Rejection reasons: novelty_epc_54
Prior art cited: ref_id: JPH033353B2
ref_type: patent
severity: background
ref_id: JP2006079272A
ref_type: patent
severity: background
ref_id: JPS5414416B2
ref_type: patent
severity: background
ref_id: - BRAIN KULIS; KATE SAENKO; TREVOR DARRELL: "What You Saw is Not What You Get: Domain Adaptation Using Asymmetric Kernel Transforms", IEEE CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION, 2011, pages 1785 - 1792, XP032038204, DOI: doi:10.1109/CVPR.2011.5995702
ref_type: npl
severity: background
ref_id: - DALAL, N.; B. TRIGGS; C. SCHMID: "Human Detection using Oriented Histograms of Flow and Appearance", IEEE EUROPEAN CONFERENCE ON COMPUTER VISION, vol. 2, 2006, pages 428 - 441, XP047385167, DOI: doi:10.1007/11744047_33
ref_type: npl
severity: background
ref_id: - MASASHI SUGIYAMA; TAKAFUMI KANAMORI; TAIJI SUZUKI; MARTHINUS CHRISTOFFEL DU PLESSIS; SONG LIU; ICHIRO TAKEUCHI: "Density-Difference Estimation", NEURAL COMPUTATION, vol. 25, no. 10, 2013, pages 2734 - 2775
ref_type: npl
severity: background
ref_id: - MAKOTO YAMADA; TAIJI SUZUKI; TAKAFUMI KANAMORI; HIROTAKA HACHIYA; MASASHI SUGIYAMA: "Relative Density-Ratio Estimation for Robust Distribution Comparison", ADVANCES IN NEURAL INFORMATION PROCESSING SYSTEMS, vol. 24, 2011, pages 594 - 602
ref_type: npl
severity: background
ref_id: US2016070986A1
ref_type: patent
severity: background
ref_id: - KATE SAENKO ET AL: "Adapting Visual Category Models to New Domains", 5 September 2010, COMPUTER VISION - ECCV 2010, SPRINGER BERLIN HEIDELBERG, BERLIN, HEIDELBERG, PAGE(S) 213 - 226, ISBN: 978-3-642-15560-4, XP019150737
ref_type: npl
severity: background
ref_id: US2016070986
ref_type: patent
severity: background
ref_id: - KATE SAENKO ET AL, Adapting Visual Category Models to New Domains, COMPUTER VISION - ECCV 2010, SPRINGER BERLIN HEIDELBERG, BERLIN, HEIDELBERG, PAGE(S) 213 - 226, (20100905), ISBN 978-3-642-15560-4, XP019150737 [X] 1,2,5,7-11 * the whole document * [I] 3,4,6
ref_type: npl
severity: novelty_destroying
Final disposition: pending (2017-12-20)