Upgrade to Pro
— share decks privately, control downloads, hide ads and more …
Speaker Deck
Features
Speaker Deck
PRO
Sign in
Sign up for free
Search
Search
ECサイトにおける閲覧履歴を用いた購買に繋がる行動の変化検出 / Change Detecti...
Search
Hiroka Zaitsu
May 15, 2020
Technology
990
1
Share
ECサイトにおける閲覧履歴を用いた購買に繋がる行動の変化検出 / Change Detection in Behavior Followed by Possible Purchase Using Electronic Commerce Site Browsing History
財津大夏, 三宅悠介
GMOペパボ株式会社 ペパボ研究所
2020.05.15 第49回 情報処理学会 インターネットと運用技術研究会
Hiroka Zaitsu
May 15, 2020
More Decks by Hiroka Zaitsu
See All by Hiroka Zaitsu
AI が Approve する開発フロー / How AI Reviewers Accelerate Our Development
zaimy
1
320
Agent Ready になるためにデータ基盤チームが今年やること / How We're Making Our Data Platform Agent-Ready
zaimy
0
240
GMOペパボのデータ基盤とデータ活用の現在地 / Current State of GMO Pepabo's Data Infrastructure and Data Utilization
zaimy
3
370
ビジネス職が分析も担う事業部制組織でのデータ活用の仕組みづくり / Enabling Data Analytics in Business-Led Divisional Organizations
zaimy
1
780
Vertex AI Matching Engine と CLIP を使って EC サービスの類似画像検索機能を作る / Development of similar image search function for EC services using Vertex AI Matching Engine and CLIP
zaimy
0
790
BigQuery の日本語データを Dataflow と Vertex AI でトピックモデリング / Topic modeling of Japanese data in BigQuery with Dataflow and Vertex AI
zaimy
1
6.3k
データサイエンティストの仕事紹介 / Data Scientist Job Introduction
zaimy
1
670
GMOペパボのサービスと研究開発を支えるデータ基盤の裏側 / Inside Story of Data Infrastructure Supporting GMO Pepabo's Services and R&D
zaimy
1
1.9k
正則化とロジスティック回帰/machine-learning-lecture-regularization-and-logistic-regression
zaimy
0
9.1k
Other Decks in Technology
See All in Technology
20260423_執筆の工夫と裏側 技術書の企画から刊行まで / From the planning to the publication of technical book
nash_efp
3
610
ハーネスエンジニアリングをやりすぎた話 ~そのハーネスは解体された~
gotalab555
5
1.9k
AgentCore Managed Harness を使ってみよう
yakumo
2
250
色を視る
yuzneri
0
170
VespaのParent Childを用いたフィードパフォーマンスの改善
taking
0
120
Class.new is all you need
riseshia
1
190
AIが自律的に働く時代へ Amazon Quick で実現するAIエージェント紹介
koheiyoshikawa
0
140
はじめての MagicPod生成AI機能 機能紹介から活用方法まで
magicpod
0
120
Practical TypeProf: Lessons from Analyzing Optcarrot
mame
0
1.2k
PicoRuby as a Multi-VM Operating System
kishima
1
220
AgentCore×VPCでの設計パターンn選と勘所
har1101
4
330
Arcana: Production-Ready RAG in Elixir @ ElixirConf EU 2026
georgeguimaraes
0
120
Featured
See All Featured
The AI Search Optimization Roadmap by Aleyda Solis
aleyda
1
5.7k
Being A Developer After 40
akosma
91
590k
The World Runs on Bad Software
bkeepers
PRO
72
12k
GraphQLとの向き合い方2022年版
quramy
50
15k
The Impact of AI in SEO - AI Overviews June 2024 Edition
aleyda
5
810
4 Signs Your Business is Dying
shpigford
187
22k
Evolving SEO for Evolving Search Engines
ryanjones
0
180
Exploring anti-patterns in Rails
aemeredith
3
330
Test your architecture with Archunit
thirion
1
2.2k
The Cost Of JavaScript in 2023
addyosmani
55
9.9k
Discover your Explorer Soul
emna__ayadi
2
1.1k
RailsConf 2023
tenderlove
30
1.4k
Transcript
ࡒେՆ, ࡾ༔հ / Pepabo R&D Institute, GMO Pepabo, Inc. 2020.05.15
ୈ49ճ ใॲཧֶձ Πϯλʔωοτͱӡ༻ٕज़ݚڀձ ECαΠτʹ͓͚ΔӾཡཤྺΛ༻͍ͨ ߪങʹܨ͕ΔߦಈͷมԽݕग़
1. ݚڀͷత 2. ՝ 3. ఏҊख๏ 4. ࣮ݧͱߟ 5. ·ͱΊͱࠓޙ
2 ࣍
1. ݚڀͷత
• ECαΠτΛ๚ΕΔϢʔβʔෳͷతΛ࣋ͭ • ྫʣʮΟϯυγϣοϐϯάʯʮͷ୳ࡧʯʮಛఆͷߪങʯͳͲ • ECαΠτͷӡӦऀ͕؍ଌՄೳͳϢʔβʔͷߦಈతʹΑͬͯมԽ͢Δ • ྫʣʮͷݕࡧʯʮͷӾཡʯʮͷߪങʯͳͲ ͷ୳ࡧ͕త ➡
ͷछྨͰݕࡧͯ͠ݕࡧ݁ՌΛϖʔδӾཡ ಛఆͷߪങ͕త ➡ ໊Ͱݕࡧͯ͠ϖʔδΛৄ͘͠Ӿཡ 4 ECαΠτͷϢʔβʔͷతͱߦಈ
• ϢʔβʔͷߦಈͷมԽʹ߹ΘͤͯECαΠτͷγεςϜΛదԠతʹ มԽͤ͞Δ͜ͱͰߪങͷ্͕ظ͞ΕΔ • Λ୳ࡧ͍ͯ͠Δ ➡ ଟ༷ੑͷ͋Δਪનख๏ʹΓସ͑ͯڵຯΛऒ͘ • ಛఆͷߪങΛߦ͓͏ͱ͍ͯ͠Δ ➡
ܾࡁಋઢΛࣔͯ͠ߪങΛଅ͢ • ECαΠτͷγεςϜͷదԠతͳมԽΛ࣮ݱ͢ΔͨΊʹɼ Ϣʔβʔ͕ԿΒ͔ͷߦಈΛऔͬͨޙʹมԽΛݕग़͍ͨ͠ 5 Ϣʔβʔͷߦಈʹ߹ΘͤͨECαΠτͷదԠతͳมԽ
• ECαΠτͷγεςϜͷదԠతͳมԽΛ࣮ݱ͢ΔͨΊʹɼ Ϣʔβʔ͕ԿΒ͔ͷߦಈΛऔͬͨޙʹมԽΛݕग़͍ͨ͠ • Ϣʔβʔ͕औΓ͏ΔߦಈECαΠτ͝ͱʹ༷ʑ • ຊใࠂͰECαΠτʹڞ௨ͷߦಈͱͯ͠ߪങʹܨ͕ΔߦಈͷมԽݕग़ΛఏҊ 6 ࠓճͷใࠂͷൣғ
2. ՝
• ECαΠτ͝ͱʹར༻Մೳͳಛྔͷ͏ͪɼͲΕΛߪങʹܨ͕Δߦಈͷ มԽݕग़ʹ༻͍Δ͖͔͕ະ • ಛྔΛશͯ༻͍ΔਂֶशHMMͳͲͷֶशϕʔεͷख๏͕͋Δ͕ɼ • ࣍ݩ͕૿͑Δ΄ͲඞཁͳαϯϓϧαΠζ͕૿େ͢Δ • Ϟσϧͷ൚ԽੑೳΛ্ͤ͞Δ͜ͱ͕ࠔʹͳΔ •
࣍ݩͷগͳ͍୯७ͳಛྔͰߦಈͷมԽΛݕग़Ͱ͖Δ͜ͱ͕·͍͠ 8 ՝ᶃมԽݕग़ʹ༻͍Δ͖ಛྔ͕ະ
• طଘݚڀʹ͓͚ΔʮϢʔβʔͷతʹରԠ͢ΔӾཡύλʔϯͷྨʯ(*1,2) • ॳظஈ֊ɿΧςΰϦʔϖʔδͱϖʔδΛଟ͘Ӿཡ͢Δ • ߪങͷલɿগͷϖʔδʹӾཡ͕ूத͢Δ • Ϣʔβʔ͝ͱͷ͋ΔظؒͷʮӾཡճʯͱʮͷछྨͷʯ ࣍ݩͷগͳ͍ಛྔʹͳΓ͏Δ *1
Moe, W.W.: Buying, searching, or browsing: Differentiating between online shoppers using in-store navigational clickstream, Journal of Consumer Psychology, Vol.13, Is-sues 1-2, pp.113-123 (2003). *2 Οϥϫϯɾυχɾμϋφ:ใ୳ࡧͷతΛߟྀͨ͠ߪങܾఆϞσϧ,ϚʔέςΟϯάɾαΠΤϯε, Vol.25, No.1,pp.15-35 (2017). 9 طଘݚڀ͔Βͷಛྔͷީิ
• Ϣʔβʔ͝ͱͷ͋ΔظؒͷʮӾཡʯͱʮͷछྨͷʯ ECαΠτϢʔβʔ͝ͱʹಛྔͷ͕औΔൣғʹࠩҟ͕͋Δ • શͯͷϢʔβʔʹֶ͍ͭͯशσʔλΛ४උ͢Δ͜ͱࠔ • ֶशෆཁͳΞϓϩʔνͰߦಈͷมԽΛݕग़͢Δ 10 ՝ᶄڥ͝ͱʹಛྔͷ͕औΔൣғʹࠩҟ͕͋Δ
3. ఏҊख๏
• ᶃߪങʹܨ͕ΔߦಈͷมԽݕग़ʹ༻͍Δ͖ಛྔ͕ະ • ࣍ݩͷগͳ͍୯७ͳಛྔͰߦಈͷมԽΛݕग़Ͱ͖Δ͜ͱ͕·͍͠ • ᶄڥ͝ͱʹಛྔͷ͕औΔൣғʹࠩҟ͕͋Γֶशσʔλͷ४උ͕ࠔ • ֶशෆཁͳΞϓϩʔνͰߦಈͷมԽΛݕग़͢Δ 12 ՝ͷཧ
• ECαΠτͷγεςϜͷదԠతͳมԽΛ࣮ݱ͢ΔͨΊʹɼ Ϣʔβʔ͕ԿΒ͔ͷߦಈΛऔͬͨޙʹมԽΛݕग़͍ͨ͠ • ᶃ࣍ݩͷগͳ͍୯७ͳಛྔΛ༻͍ͯᶄֶशෆཁͳΞϓϩʔνͰ ߪങʹܨ͕ΔߦಈͷมԽݕग़Λߦ͏ • ᶃͷӾཡճʹର͢Δͷଐੑͷछྨͷൺ • ઌߦݚڀΑΓɼ͜ͷߪങʹ͚ͯখ͘͞ͳΔͱԾఆ
• ᶄ౷ܭతԾઆݕఆʹΑΔฏۉͷࠩͷݕఆ 13 ఏҊख๏
• ͷӾཡճʹର͢Δͷଐੑͷछྨͷൺ • Ϣʔβʔ ͷߦಈཤྺ • ʹӾཡ ݕࡧ ͳͲ͕͋Δ •
ͷҙͷҐஔͷΟϯυ Λߟ͑Δ • ୠ͠ɼΟϯυαΠζ ͱ ͔ͭ Λຬͨ͢࠷খͷࣗવ Λ༻͍ͯ u Su = (a1 , a2 , …, al ) a aview asearch Su Wu (t) = (a′ 1 , a′ 2 , a′ 3 , …, at ) w 1 < n < w t − w + n > 0 n a′ 1 = at−w+n a′ 2 = at−w+n+1 a′ 3 = at−w+n+2 14 ಛྔͷఆٛᶃ
• ͷӾཡճʹର͢Δͷଐੑͷछྨͷൺ • ͷҙͷҐஔͷΟϯυ ʹ͓͚Δ • ͷଐੑ ͷछྨʹؔ͢Δू߹ Λ༻͍ͯ ಛྔ
• ͕খ͍͞΄Ͳߪങʹ͔͍ͬͯΔ Su Wu (t) = (a′ 1 , a′ 2 , a′ 3 , …, at ) aview ͷରͱͳͬͨͷଐੑ attr ͷछྨ ͷӾཡ aview ͷճ attr rattr(Wu (t)) = || count(aview) 15 ಛྔͷఆٛᶄ
• Ϣʔβʔɹͷߦಈཤྺ • ͰͷIDʹؔ͢Δಛྔ • ͱ ͷରͷID=1ɼ ͷରͷID=2ͱ͢Δͱ Su =
(asearch 1 , aview 2 , aview 3 , asearch 4 , aview 5 , aview 6 , aview 7 , aview 8 , aview 9 , apurchase 10 ) Wu (5) = (asearch 1 , aview 2 , aview 3 , asearch 4 , aview 5 ) aview 2 aview 3 aview 5 rID(Wu (5)) = || count(aview) = 2 3 16 ಛྔͷྫ u Wu (5)
• ಛྔͷਪҠͷΟϯυ Λߟ͑Δ • ୠ͠ɼΟϯυαΠζ ͱ ͔ͭ Λຬͨ͢࠷খͷࣗવ Λ༻͍ͯ(*) •
ΛҙͷͰೋͨ͠Οϯυ ͱ ʹରͯ͠ ౷ܭతԾઆݕఆʹΑΔฏۉͷࠩͷݕఆΛద༻ • ༗ҙਫ४ Ͱ༗ҙࠩ͋Γͱݟͳͨ͠߹ʹ ͷ࠷ॳͷཁૉΛมԽͱݟͳ͢ * r' ΛٻΊΔࣜΛݚڀใࠂͷ͔࣌Βमਖ਼͍ͯ͠·͢ W′ u (t) = (r′ 1 , r′ 2 , r′ 3 , …, rattr(Wu (t))) w′ 1 < m < w′ t − w′ + m > 0 m r′ 1 = rattr(Wu (t − w′ + m)) r′ 2 = rattr(Wu (t − w′ + m + 1)) r′ 3 = rattr(Wu (t − w′ + m + 2)) W′ u (t) W′ 1 W′ 2 s W′ 2 17 ಛྔͷਪҠΛ༻͍ͨมԽݕग़ͷఆٛᶃ
• ౷ܭతԾઆݕఆʹΑΔฏۉͷࠩͷݕఆʹ Welch ͷ ݕఆΛ༻͍Δ • Student ͷ ݕఆͷվྑ •
ࢄ͕͍͜͠ͱΛԾఆ͠ͳ͍ • ͷΈʹରԠ͕Մೳ • ඪຊͷࢄ͕͘͠ͳ͍߹ʹൣʹରԠ͠͏Δ t t 18 ಛྔͷਪҠΛ༻͍ͨมԽݕग़ͷఆٛᶄ
• ͷͱ͖ ͷ֤ʹ Welch ͷ ݕఆΛద༻ • ͱ ͷͰ༗ҙࠩ͋Γͱݟͳͨ͠߹ ͷ࣌ࠁ
ΛมԽͱݟͳ͢ W′ u (t) = (r′ 1 , r′ 2 , r′ 3 , r′ 4 , r′ 5 ) W′ 1 = (r′ 1 ) W′ 2 = (r′ 2 , r′ 3 , r′ 4 , r′ 5 ) W′ 1 = (r′ 1 , r′ 2 ) W′ 2 = (r′ 3 , r′ 4 , r′ 5 ) W′ 1 = (r′ 1 , r′ 2 , r′ 3 ) W′ 2 = (r′ 4 , r′ 5 ) W′ 1 = (r′ 1 , r′ 2 , r′ 3 , r′ 4 ) W′ 2 = (r′ 5 ) t W′ 1 = (r′ 1 , r′ 2 ) W′ 2 = (r′ 3 , r′ 4 , r′ 5 ) r′ 3 = rattr(Wu (t − w′ + m + 2)) t 19 ಛྔͷਪҠΛ༻͍ͨมԽݕग़ͷྫ
4. ࣮ݧͱߟ
• ࣮ࡍͷECαΠτͷӾཡཤྺʹ͓͚ΔఏҊख๏ͷ༗ޮੑͷݕূ • GMOϖύϘגࣜձࣾͷӡӦ͢ΔECαΠτʮminneʯͷӾཡཤྺʹద༻ͨ͠ 1. ϋΠύʔύϥϝʔλͷݕ౼ 2. ఏҊख๏ʹదͨ͠࡞ଐੑͷߟ 3. ݸผͷϢʔβʔʹର͢ΔมԽݕग़ͷ݁Ռͷ֬ೝ
• ECαΠτͷߦಈੳʹ༻͍ΒΕΔӅΕϚϧίϑϞσϧͱͷਫ਼ͷൺֱ • ܭࢉ࣌ؒͷ֬ೝ ࣮ݧͷతͱํ๏ 21
• ECαΠτʮminneʯͷϓϩμΫγϣϯڥʹ͓͚ΔӾཡཤྺ • 20203݄10͔࣌Β24࣌·Ͱͷσʔλ • Ӿཡཤྺ ͷܥྻ ͷ 96,984 Ϣʔβʔ
• ൺֱͷͨΊߪങΛߦͬͨϢʔβʔͱߦΘͳ͔ͬͨϢʔβʔʹׂ • ࡞ʹඥͮ͘4ͭͷଐੑͰ࣮ݧ • ࡞IDɼ࡞ͷग़ऀIDɼ࡞ͷΧςΰϦάϧʔϓɼ࡞ͷΧςΰϦ Su l ≥ 6 σʔληοτ 22
• ΧςΰϦάϧʔϓ • ྫʣʮϑΝογϣϯʯΧςΰϦάϧʔϓͷΧςΰϦ • TγϟπɼϫϯϐʔεɼτοϓεɼίʔτɼεΧʔτ ͳͲ ࡞ଐੑ - ࡞ͷΧςΰϦάϧʔϓͱΧςΰϦ
23
ϋΠύʔύϥϝʔλͷݕ౼ • Ӿཡཤྺ͔ΒಛྔͷΛٻΊΔࡍͷΟϯυͷ෯ Λ {5,10} Ͱ࣮ݧ • ಛྔͷͷมԽΛݕग़͢ΔࡍͷΟϯυͷ෯ Λ {3,5}
Ͱ࣮ݧ • ߪങϢʔβʔʹؔͯ͠ΑΓଟ͘ͷมԽΛݕग़͠ɼඇߪങϢʔβʔʹؔͯ͠ গͳ͍มԽΛݕग़ͨ͠ ͱ ΛҎ߱ͷ࣮ݧʹ༻͍ͨ • ༗ҙਫ४ • ׳ྫతͳͱͯ͠ Λ༻͍ͨ w w′ w = 10 w′ = 5 s s = 0.05 24
• ࡞ଐੑ͝ͱͷಛྔͷͷਪҠΛശͻ͛ਤͰ֬ೝ • ྫ ఏҊख๏ʹద͢Δ࡞ଐੑͷߟ 25 • ԣ࣠ɿ࣌ܥྻ • ॎ࣠ɿಛྔͷ
• ശͷ্ɿୈࡾ࢛Ґ • ശͷԼɿୈҰ࢛Ґ • ശͷதͷԣઢɿதԝ • ͻ͛ͷ্ɿୈࡾ࢛Ґʴ࢛Ґൣғͷ1.5ഒ • ͻ͛ͷԼɿୈҰ࢛Ґ−࢛Ґൣғͷ1.5ഒ • ͻ͛ͷ্Լͷɿ֎Ε • ͍ॎઢɿதԝʹରͯ͠ఏҊख๏Λద༻ͯ͠ݕग़ͨ͠มԽ
ఏҊख๏ʹద͢Δ࡞ଐੑ ߪങϢʔβʔ ඇߪങϢʔβʔ ࡞*% ࡞ͷग़ऀ*% 26 • ߪങϢʔβʔɿಛྔͷ͕Լ͕ΔʹมԽΛݕग़ • ඇߪങϢʔβʔɿ΄΅มԽΛݕग़͍ͯ͠ͳ͍ʢߦಈͷॳظಛྔͷͷมಈ͕େ͖͍ͨΊ1Օॴݕग़ʣ
➡ ఏҊख๏ͷಛྔʹ༻͍Δ࡞ଐੑͱͯ͠ద͍ͯ͠Δ
ఏҊख๏ʹద͞ͳ͍࡞ଐੑ ߪങϢʔβʔ ඇߪങϢʔβʔ ࡞ͷΧςΰϦάϧʔϓ ࡞ͷΧςΰϦ 27 • ߪങϢʔβʔͱඇߪങϢʔβʔͷ྆ํͰ࣌ܥྻͷॳظʹಛྔͷ͕Լ͕ΓɼͦͷޙมԽ͠ͳ͘ͳΔ • minne
ͰΧςΰϦͷߜΓࠐΈ͕ߪങͷ༗ແͱؔͳ͘ߦಈͷॳظʹߦΘΕΔ ➡ ఏҊख๏ͷಛྔʹ༻͍Δ࡞ଐੑͱͯ͠ద͍ͯ͠ͳ͍
ӅΕϚϧίϑϞσϧʢHMMʣͱͷൺֱᶃ • ݸผͷϢʔβʔʹର͢Δਫ਼ͷݕ౼ • Ϟσϧͷग़ྗΛ༧ଌϥϕϧʮߪങϢʔβʔʯʹϚοϐϯά͢Δ • ఏҊख๏ɿมԽΛݕग़ͨ͠߹ • HMMɿӅΕঢ়ଶ2ͷ͏ͪಛྔͷͷฏۉ͕͍ঢ়ଶʹભҠͨ͠߹ •
HMMͷϞσϧͷߏஙͷͨΊσʔληοτΛ9:1ʹׂ • ܇࿅σʔλɿ87,285Ϣʔβʔ • ςετσʔλɿ9,523Ϣʔβʔ 28
ӅΕϚϧίϑϞσϧʢHMMʣͱͷൺֱᶄ • ఏҊख๏ΑΓHMMͷํ͕ੵۃతʹʮߪങϢʔβʔʯͷϥϕϧΛ͚ͨ ࡞IDΛಛྔʹ༻͍ͨ߹ͷࠞಉߦྻ ਖ਼ղϥϕϧ ߪങ ඇߪങ ༧ଌϥϕϧ ఏҊख๏ ߪങ
526 4551 ඇߪങ 201 4245 HMM ߪങ 662 5571 ඇߪങ 65 3225 ࡞ͷग़ऀIDΛಛྔʹ༻͍ͨ߹ͷࠞಉߦྻ ਖ਼ղϥϕϧ ߪങ ඇߪങ ༧ଌϥϕϧ ఏҊख๏ ߪങ 483 5719 ඇߪങ 244 3077 HMM ߪങ 679 7047 ඇߪങ 48 1749 29
ӅΕϚϧίϑϞσϧʢHMMʣͱͷൺֱᶅ • ఏҊख๏ • ਅͷඇߪങϢʔβʔʹର͢Δਫ਼͕ߴ͍ • ِཅੑʹରِͯ͠ӄੑ͕͍ • ߪങʹܨ͕ΔϢʔβʔͷߦಈͷมԽݕग़ͷతʹԊ͍ͬͯΔ •
HMM • ਅͷߪങϢʔβʔʹର͢Δਫ਼͕ߴ͍ • ʮߪങ͠ͳ͔ͬͨʯʹϚοϐϯά͞ΕΔӅΕঢ়ଶͷ͕ฏۉ1.0ɼඪ४ภࠩ1.16*10−8ͱͳͬͯ ͓Γɼ͔ᷮͰಛྔͷ͕ݮগ͢Δͱʮߪങͨ͠ʯӅΕঢ়ଶʹભҠ͍ͯͨ͠ 30
ܭࢉ࣌ؒ • 3.1GHz ΫΞουίΞ Intel Core i7 Λར༻͢ΔධՁڥʹ͓͍ͯɼΟϯυ ͋ͨΓͷܭࢉ࣌ؒ1.71ϛϦඵʙ1.75ϛϦඵ
• ΣϒαΠτͷಡΈࠐΈ࣌ؒ1,000ϛϦඵະຬ͕·͍͠ͱ͞Ε͓ͯΓɼఏ Ҋख๏ʹΑΔมԽݕग़ʹֻ͔Δ࣌ؒेʹখ͍͞ W′ u (t) 31
5. ·ͱΊͱࠓޙ
·ͱΊ • ߪങʹܨ͕ΔϢʔβʔͷߦಈͷมԽݕग़ • Ӿཡཤྺ͔ΒಛྔΛ࡞ͯ͠౷ܭతԾઆݕఆʹΑͬͯมԽݕग़Λߦ͏ • ࣮ࡍͷECαΠτͷσʔλΛ༻͍ͯಛྔʹ༻͍Δଐੑͷݕ౼ͱਫ਼͓Α ͼܭࢉ࣌ؒͷ֬ೝΛߦͬͨ • HMMͱͷൺֱͰඇߪങϢʔβʔʹؔ͢Δਫ਼ʹ্ؔͯ͠ճΓɼࣄલͷֶश
͕ෆཁ 33
ࠓޙʹ͍ͭͯ • ఏҊख๏ͷਫ਼ͷվળ • ಛྔͷ͕มԽ͢Δࡍͷਖ਼ෛํͷϞσϧͷΈࠐΈ • ಛྔͷͷมಈ͕େ͖͍ظؒͷআ֎ͳͲ • ܭࢉ࣌ؒͷॖ •
มԽݕग़ʹ༻͍ΔΟϯυΛ֤ཁૉͰׂͤͣҰՕॴͰׂ͢Δ • খඪຊʹରͯ͠ؤ݈ͳ౷ܭతԾઆݕఆͷख๏ͷݕ౼ 34