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
1
930
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
Tweet
Share
More Decks by Hiroka Zaitsu
See All by Hiroka Zaitsu
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
440
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
740
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
5.8k
データサイエンティストの仕事紹介 / Data Scientist Job Introduction
zaimy
1
600
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
1.8k
正則化とロジスティック回帰/machine-learning-lecture-regularization-and-logistic-regression
zaimy
0
8.7k
trinity で Cloud Composer に ワークフローを簡単デプロイ / Easy workflow deployment to Cloud Composer with trinity
zaimy
0
880
ハンドメイド作品を対象としたECサイトにおける大量生産品の検出 / Detection of Mass-produced Goods at EC Site to Trade Handmade Goods
zaimy
3
4.8k
キャリアキーノート2018 / Career Keynote 2018
zaimy
1
2.2k
Other Decks in Technology
See All in Technology
Amazon SNSサブスクリプションの誤解除を防ぐ
y_sakata
3
190
Microsoft Fabric ガバナンス設計の一歩目を考える
ryomaru0825
1
150
LIXIL基幹システム刷新に立ち向かう技術的アプローチについて
tsukuha
1
900
ゼロから始めるSREの事業貢献 - 生成AI時代のSRE成長戦略と実践 / Starting SRE from Day One
shinyorke
PRO
0
190
Amplify Gen2から知るAWS CDK Toolkit Libraryの使い方/How to use the AWS CDK Toolkit Library as known from Amplify Gen2
fossamagna
1
390
[SRE NEXT] ARR150億円_エンジニア140名_27チーム_17プロダクトから始めるSLO.pdf
satos
6
3.4k
ClaudeCodeにキレない技術
gtnao
1
930
How Do I Contact Jetblue Airlines® Reservation Number: Fast Support Guide
thejetblueairhelpsupport
0
210
Deep Security Conference 2025:生成AI時代のセキュリティ監視 /dsc2025-genai-secmon
mizutani
5
3.4k
Semantic Machine Intelligence for Vision, Language, and Actions
keio_smilab
PRO
2
340
(HackFes)米国国防総省のDevSecOpsライフサイクルをAWSのセキュリティサービスとOSSで実現
syoshie
5
590
第64回コンピュータビジョン勉強会「The PanAf-FGBG Dataset: Understanding the Impact of Backgrounds in Wildlife Behaviour Recognition」
x_ttyszk
0
260
Featured
See All Featured
Dealing with People You Can't Stand - Big Design 2015
cassininazir
367
26k
Building Flexible Design Systems
yeseniaperezcruz
328
39k
Rails Girls Zürich Keynote
gr2m
95
14k
Designing Experiences People Love
moore
142
24k
RailsConf & Balkan Ruby 2019: The Past, Present, and Future of Rails at GitHub
eileencodes
138
34k
Optimizing for Happiness
mojombo
379
70k
RailsConf 2023
tenderlove
30
1.2k
Producing Creativity
orderedlist
PRO
346
40k
A better future with KSS
kneath
238
17k
10 Git Anti Patterns You Should be Aware of
lemiorhan
PRO
656
60k
Principles of Awesome APIs and How to Build Them.
keavy
126
17k
Building Applications with DynamoDB
mza
95
6.5k
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