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これまでの研究紹介と博士課程での研究計画について/research-plan-presenta...
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monochromegane
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これまでの研究紹介と博士課程での研究計画について/research-plan-presentation-for-publish
利用者や情報システムの文脈に応じて自動かつ継続的に提案を最適化する適応的な推薦システム
2020.07.20 令和2年度 情報知能工学専攻 博士後期課程 入学試験試問
monochromegane
July 20, 2020
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Transcript
ར༻ऀใγεςϜͷจ຺ʹԠͯ͡ ࣗಈ͔ͭܧଓతʹఏҊΛ࠷దԽ͢ΔదԠతͳਪનγεςϜ ࡾ༔հ / Pepabo R&D Institute, GMO Pepabo, Inc.
2020.07.20 ྩ2 ใೳֶઐ߈ ത࢜ޙظ՝ఔ ೖֶࢼݧࢼ ͜Ε·Ͱͷݚڀհͱ ത࢜՝ఔͰͷݚڀܭըʹ͍ͭͯ
1. ུྺ 2. ͜Ε·Ͱͷݚڀհ 3. ത࢜՝ఔͰͷݚڀܭը 2 ࣍
1. ུྺ
• ࡾ༔հ • 20033݄ ࡚େֶ ڥՊֶ෦ڥࡦίʔε ଔۀ • ݩԬͷSIerۈΛܦͯɺ2012ΑΓגࣜձࣾpaperboy&co.(ݱGMOϖύ Ϙגࣜձࣾ)ʹۈɻࢿ࢈ཧγεςϜΠϯλʔωοταʔϏεʹ͓͚Δ
WebΞϓϦέʔγϣϯͷ։ൃɾӡ༻ҡ࣋ۀʹैࣄɻ • 2017ΑΓಉࣾͷݚڀ৬ɻใγεςϜͷࣗదԠͷݚڀʹैࣄɻ 4 ུྺ
2. ͜Ε·Ͱͷݚڀհ
ݚڀίϯηϓτ - ใγεςϜͷࣗదԠ -
• ଟ༷͔ͭܧଓతʹมԽ͢ΔڥͷதͰɺใγεςϜ͕ܧଓతʹػೳ͢Δʹ ɺͦͷߏϩδοΫΛߋ৽͠มԽʹै͢Δඞཁ͕͋Δ • → ྫʣใγεςϜͷෛՙঢ়گɺར༻ऀͷߦಈͷมԽ • ͜Ε·Ͱɺ͜ͷैɺӡ༻ҡ࣋ۀͱͯ͠ӡ༻ऀ͕୲͖ͬͯͨ 7 ใγεςϜͱڥมԽ
• ଟ༷͔ͭܧଓతʹมԽ͢ΔڥͷதͰɺใγεςϜ͕ܧଓతʹػೳ͢Δʹ ɺͦͷߏϩδοΫΛߋ৽͠มԽʹै͢Δඞཁ͕͋Δ • → ྫʣใγεςϜͷෛՙঢ়گɺར༻ऀͷߦಈͷมԽ • ͜Ε·Ͱɺ͜ͷैɺӡ༻ҡ࣋ۀͱͯ͠ӡ༻ऀ͕୲͖ͬͯͨ 8 ใγεςϜͱڥมԽ
• ਓखʹΑΔڥͷมԽݕใγεςϜͷߋ৽ɺैͷ࣌ؒࠩΛ͏ • ݁Ռͱͯ͠ɺ҆ఆੑར༻ऀͷຬͷԼɺӡ༻ऀͷෛ୲ͷ૿ՃΛট͘
• ैདྷͷӡ༻ҡ࣋ͷऔΓΈͰɺใγεςϜΛڥͷมԽʹରԠͤ͞Δͨ ΊɺਓʹΑΔܦݧଇஅͱ෦తͳࣗಈԽʹཹ·͍ͬͯΔ • → ྫʣܦݧଇʹΑΔᮢઃఆɺԽͨ͠ར༻ऀͷߦಈୡʹΑΔஅ 9 ڥมԽʹࣗΒదԠ͢ΔใγεςϜʹ͚ͯ • ਓʹΑΔஅߋ৽ͷఔΛࣗಈԽ͠ɺใγεςϜࣗମ͕ڥมԽΛଊ͑ม
Խʹै͢ΔదԠతͳΈͷݚڀ • ͳΒͼʹ࣮ӡ༻ͷద༻ ݚڀίϯηϓτ
ݚڀհ
11 ݚڀ࣮ͱҐஔ͚ (2017ʙݱࡏ) No. ໊ ݚ ձ ࠃ 1
ಛநग़ثͷֶशͱߪങཤྺΛඞཁͱ͠ͳ͍ྨࣅը૾ʹΑΔؔ࿈ݕࡧγεςϜ ◦ 2 ΞΫηεස༧ଌʹجͮ͘ԾαʔόͷܭըతΦʔτεέʔϦϯά ◦ 3 Sanny: େنECαΠτͷͨΊͷਫ਼ͱΛཱ྆ͨ͠ࢄՄೳͳۙࣅۙ୳ࡧΤϯδϯ ◦ 4 ΞΫηεස༧ଌʹجͮ͘ԾαʔόͷܭըతΦʔτεέʔϦϯά ◦ 5 ར༻ऀͷจ຺ʹԠͯ͡ܧଓతʹਪનख๏ͷબΛ࠷దԽ͢ΔਪનγεςϜ ◦ 6 Optimization for Number of goroutines Using Feedback Control ◦ 7 ͳΊΒ͔ͳηΩϡϦςΟΛࢦͯ͠ ◦ 8 Kaburaya AutoScaler: ଟڥͰͷӡ༻ੑΛߟྀͨࣗ͠దԠܕΦʔτεέʔϦϯά੍ޚܥ ◦ 9 Synapse: จ຺ʹԠͯ͡ܧଓతʹਪનख๏ͷબΛ࠷దԽ͢ΔਪનγεςϜ ◦ 10 ඇఆৗͳଟόϯσΟοτʹ͓͚ΔมԽݕग़ΞϓϩʔνͷઢܗϞσϧͷ֦ு ◦ ˎ ݚ: ݚڀใࠂɺձ: ϑΥʔϥϜɺγϯϙδϜɺ: δϟʔφϧɺࠃ: ࠃࡍձٞʢϓϩάϥϛϯάݴޠΧϯϑΝϨϯεʣ
ΦʔτεέʔϦϯάख๏
13 ࠶ܝ: ݚڀ࣮ͱҐஔ͚ (2017ʙݱࡏ) No. ໊ ݚ ձ ࠃ
1 ಛநग़ثͷֶशͱߪങཤྺΛඞཁͱ͠ͳ͍ྨࣅը૾ʹΑΔؔ࿈ݕࡧγεςϜ ◦ 2 ΞΫηεස༧ଌʹجͮ͘ԾαʔόͷܭըతΦʔτεέʔϦϯά ◦ 3 Sanny: େنECαΠτͷͨΊͷਫ਼ͱΛཱ྆ͨ͠ࢄՄೳͳۙࣅۙ୳ࡧΤϯδϯ ◦ 4 ΞΫηεස༧ଌʹجͮ͘ԾαʔόͷܭըతΦʔτεέʔϦϯά ◦ 5 ར༻ऀͷจ຺ʹԠͯ͡ܧଓతʹਪનख๏ͷબΛ࠷దԽ͢ΔਪનγεςϜ ◦ 6 Optimization for Number of goroutines Using Feedback Control ◦ 7 ͳΊΒ͔ͳηΩϡϦςΟΛࢦͯ͠ ◦ 8 Kaburaya AutoScaler: ଟڥͰͷӡ༻ੑΛߟྀͨࣗ͠దԠܕΦʔτεέʔϦϯά੍ޚܥ ◦ 9 Synapse: จ຺ʹԠͯ͡ܧଓతʹਪનख๏ͷબΛ࠷దԽ͢ΔਪનγεςϜ ◦ 10 ඇఆৗͳଟόϯσΟοτʹ͓͚ΔมԽݕग़ΞϓϩʔνͷઢܗϞσϧͷ֦ு ◦ ˎ ݚ: ݚڀใࠂɺձ: ϑΥʔϥϜɺγϯϙδϜɺ: δϟʔφϧɺࠃ: ࠃࡍձٞʢϓϩάϥϛϯάݴޠΧϯϑΝϨϯεʣ
• ใγεςϜͷӡ༻ʹ͓͍ͯɺॲཧੑೳΛอͪͭͭඞཁ࠷খݶͷαʔόΛ༻͍ Δ͜ͱͰӡ༻ίετΛ੍ޚ͢Δ͜ͱॏཁ • มಈ͢Δαʔόधཁʹै͢ΔͨΊΦʔτεέʔϦϯάػೳΛಋೖ 14 എܠ • ॲཧੑೳΛอͭඞཁ࠷খݶͷαʔόܦݧͱಓͳνϡʔχϯάͰݸผʹ ٻΊΔ͕ɺใγεςϜͷมߋཧରͷ૿Ճʹै͍ࠔʹͳΔ
• ·ͨɺͷࢉग़ʹΦʔτεέʔϦϯάͷ࣮ߦ࣌ͷ࣌ؒࠩͷߟྀඞཁ ӡ༻্ͷ՝
• ܧଓతʹมߋ͞Ε͏ΔෳͷใγεςϜʹରͯ͠ɺΕߟྀͨ͠Φʔτε έʔϦϯάͷ࠷దͳ݅Λܧଓͯ͠ٻΊΔ͜ͱ͕ӡ༻ͷෛ୲ • ใγεςϜΛߏ͢ΔαʔόͷॲཧੑೳΛࣗಈͰѲ͠ɺใγεςϜͷॲ ཧੑೳΛอͭඞཁ࠷খݶ͔ͭΕΛߟྀͨ͠αʔόΛࢉग़͍ͨ͠ • αʔόͷॲཧੑೳΛ࣮ߦ࣌ʹࣗಈ͔ͭܧଓతʹਪఆ͠ɺΦʔτεέʔϦϯάͷ Εߟྀͨ͠࠷దͳαʔόΛࢉग़͢Δ੍ޚܥ 15
ݚڀͷతͱఏҊͷࠎࢠ
• ଟڥͰͷӡ༻ੑΛߟྀͨࣗ͠దԠܕΦʔτεέʔϦϯά੍ޚܥ[*] 16 ఏҊख๏ (Kaburaya AutoScaler) < >ࡾ༔հ ܀ྛ݈ଠ ,BCVSBZB"VUP4DBMFSଟڥͰͷӡ༻ੑΛߟྀͨࣗ͠దԠܕΦʔτεέʔϦϯά੍ޚܥ
Πϯλʔωοτ ͱӡ༻ٕज़γϯϙδϜจू QQ /PW
• ଟڥͰͷӡ༻ੑΛߟྀͨࣗ͠దԠܕΦʔτεέʔϦϯά੍ޚܥ • M: αʔόॲཧੑೳΛɺ୯Ґ࣌ؒ͋ͨΓͷॲཧͷ্ݶ͔ΒٻΊΔ • D: ༧ΊఆΊͨΕظؒʹର͠ɺݱࡏͷཁٻॲཧͱαʔόॲཧੑೳ͔Βෆ ͢Δͱߟ͑ΒΕΔະॲཧཁٻΛٻΊΔ •
F: ݱࡏͷॲཧཁٻʹະॲཧཁٻΛՃ͑ɺαʔόॲཧੑೳ͔Βඞཁͳ αʔόΛࢉग़ 17 ఏҊख๏ (Kaburaya AutoScaler)
18 ఏҊख๏ͷධՁ αʔόੑೳʢॲཧ্ݶʣͷਪఆධՁ ෛՙ࣌Ұ࣌తʹαʔό͋ͨΓͷෛՙ͕ߴ·Δෛ ՙ૿Ճ࣌Ͱ҆ఆͯ͠ਪఆʢ࣮ઢʣɻ αʔόͷैੑධՁ ੨ઢͷཧαʔόʹैɻΕΛߟྀ͠ɺఆ͞ ΕΔະॲཧͷཁٻΛॲཧՄೳͳαʔόΛೖɻ ະॲཧཁٻͷղফ݁ՌͷධՁ ΕʹΑΓੵ࣮ͨ͠ઢͷະॲཧཁٻΛଈ࣌ղফɻ
ഁઢΕରࡦΛ͠ͳ͍߹ͷਪҠɻ
ਪનγεςϜ
20 ࠶ܝ: ݚڀ࣮ͱҐஔ͚ (2017ʙݱࡏ) No. ໊ ݚ ձ ࠃ
1 ಛநग़ثͷֶशͱߪങཤྺΛඞཁͱ͠ͳ͍ྨࣅը૾ʹΑΔؔ࿈ݕࡧγεςϜ ◦ 2 ΞΫηεස༧ଌʹجͮ͘ԾαʔόͷܭըతΦʔτεέʔϦϯά ◦ 3 Sanny: େنECαΠτͷͨΊͷਫ਼ͱΛཱ྆ͨ͠ࢄՄೳͳۙࣅۙ୳ࡧΤϯδϯ ◦ 4 ΞΫηεස༧ଌʹجͮ͘ԾαʔόͷܭըతΦʔτεέʔϦϯά ◦ 5 ར༻ऀͷจ຺ʹԠͯ͡ܧଓతʹਪનख๏ͷબΛ࠷దԽ͢ΔਪનγεςϜ ◦ 6 Optimization for Number of goroutines Using Feedback Control ◦ 7 ͳΊΒ͔ͳηΩϡϦςΟΛࢦͯ͠ ◦ 8 Kaburaya AutoScaler: ଟڥͰͷӡ༻ੑΛߟྀͨࣗ͠దԠܕΦʔτεέʔϦϯά੍ޚܥ ◦ 9 Synapse: จ຺ʹԠͯ͡ܧଓతʹਪનख๏ͷબΛ࠷దԽ͢ΔਪનγεςϜ ◦ 10 ඇఆৗͳଟόϯσΟοτʹ͓͚ΔมԽݕग़ΞϓϩʔνͷઢܗϞσϧͷ֦ு ◦ ˎ ݚ: ݚڀใࠂɺձ: ϑΥʔϥϜɺγϯϙδϜɺ: δϟʔφϧɺࠃ: ࠃࡍձٞʢϓϩάϥϛϯάݴޠΧϯϑΝϨϯεʣ
21 എܠ • ใγεςϜʹ͓͚ΔใաଟΛղܾ͢ΔɺਪનγεςϜͷಋೖ • → ͳΜΒ͔ͷํࡦʢ= ਪનख๏ʣʹج͖ͮଟͷબࢶ͔Βར༻ऀ͕ڵຯ Λ࣋ͭͷΛఏҊ͢ΔγεςϜ •
ӡ༻ऀʹͱͬͯɺޮՌతͳʮਪનख๏ʯͷબ͕ॏཁ • ޮՌతͳਪનख๏ঢ়گʹΑͬͯҟͳΔ • ͔͠͠ͳ͕Βɺ࣮ڥͰͷܧଓతͳਪનख๏ͷධՁʹػձଛࣦ͕͏ ӡ༻্ͷ՝
• ਪનख๏ͷ༏ྼଟ͘ͷཁҼ͔ΒͳΔঢ়گʢ=จ຺ʣʹΑͬͯࠨӈ͞ΕΔ • ޮՌతͳਪનख๏Λػձଛࣦ͕ͳ͍Α͏ʹจ຺ʹԠ͍͚͍ͯͨ͡ • ࣄલʹఆΊͨจ຺͝ͱʹਪનख๏ͷબΛࣗಈ͔ͭܧଓతʹ࠷దԽ͢Δ ϝλਪનγεςϜ • → ࠷ળͳਪનख๏ͷબΛଟόϯσΟοτͱΈͳͯ͠ղ͘
22 ݚڀͷతͱఏҊͷࠎࢠ
• ʮʯͱݺΕΔෳͷީิ͔ΒಘΒΕΔใुΛ࠷େԽ͢Δ • ϓϨΠϠʔҰͷࢼߦͰ1ͭͷΛબ͠ɺใुΛಘΔ • ͦΕͧΕͷ͋Δใुʹै͍ใुΛੜ • ͨͩ͠ɺϓϨΠϠʔ͜ͷใुΛࢼߦͷ݁Ռ͔Βਪଌ͢Δඞཁ͕͋Δ 23 ଟόϯσΟοτ
• ϓϨΠϠʔ͋Δ࣌ͷͷධՁʹج͖ͮʮ׆༻ʯͱʮ୳ࡧʯΛฒߦͯ͠ߦ͏ • ͜ͷτϨʔυΦϑΛղফ͢ΔͨΊʹ༷ʑͳղ๏͕ఏҊ͞Ε͍ͯΔ
• ͝ͱʹෳͷจ຺͕͋Γɺจ຺ʹԠͯ͡ใु͕ܾ·ΔଟόϯσΟοτ ͷઃఆ • ຊݚڀใࠂͰɺจ຺ɺෳͷཁҼͷύϥϝʔλͷΈ߹ΘͤͰදݱ͞ Εͨঢ়ଶͷ͜ͱΛࢦ͢ • → ཁҼύϥϝʔλͷ͕{0,1}ͷ߹ɺจ຺ཁҼ ʹରͯ͠
ύλʔϯ d 2d 24 ઢܗͳଟόϯσΟοτ • ઢܗͳଟόϯσΟοτͷղ๏Ͱɺจ຺ͷ֬Ͱͳ͘ɺཁҼ͝ͱ ͷʢઢܗύϥϝʔλʣΛਪఆ͢Δ͜ͱͰ֤จ຺ʹ͓͚ΔใुΛ༧͢Δ
• จ຺͝ͱʹਪનख๏ͷબΛࣗಈ͔ͭܧଓతʹ࠷దԽ͢ΔϝλਪનγεςϜ[1] • จ຺͝ͱͷ࠷ળͳબΛɺઢܗͳଟόϯσΟοτͷղ๏Ͱ͋Δ Linear Thompson SamplingΛ༻͍ͯղ͘ • จ຺ͱͯ͠ɺᶃใγεςϜͷ࣌ؒͷܦաɺᶄਪનରͷಛੑͷࠩҟΛ ѻ͏
• จ຺͝ͱʹબͨ͠ਪનख๏ͱ͜Εʹର͢Δར༻ऀͷԠΛه͠ɺબ ͷվળʹ༻͍Δ 25 ఏҊγεςϜ (Synapse) <>ࡾ༔հ ็߃ݑ 4ZOBQTFจ຺ʹԠͯ͡ܧଓతʹਪનख๏ͷબΛ࠷దԽ͢ΔਪનγεςϜ ిࢠใ௨৴ֶձจࢽ% 7PM+% /P QQ /PW UPBQQFBS
26 ఏҊγεςϜ (Synapse)
• ࣮αʔϏεͷӡ༻σʔλΛ༻͍ͨγϛϡϨʔγϣϯʹ͓͍ͯɺจ຺Λߟྀ͠ͳ ͍ͷͱൺֱͯ͠ɺྦྷੵΫϦοΫ͕2%্͢Δ͜ͱΛ֬ೝ[1] • ֘γεςϜ࣮αʔϏεͰՔಇɾܧଓతʹධՁத • ࠓޙɺऔΓѻ͑Δจ຺ɺਪનख๏Λ͍͛ͯ͘[3][4] • ߹ΘͤͯɺڥมԽͷैੑΛ্͍ͤͯ͘͞[2] 27
ఏҊγεςϜͷධՁ <>ࡒେՆɼࡾ༔հɼ&$αΠτʹ͓͚ΔӾཡཤྺΛ༻͍ͨߪങʹܨ͕ΔߦಈͷมԽݕग़ɼݚڀใࠂΠϯλʔωοτͱӡ༻ٕज़ *05 ɼ WPM*05ɼQQrɼ <>ଜ໋ɼࡾ༔հɼϋϯυϝΠυ࡞Λରͱͨ͠&$αΠτʹ͓͚Δ୯ޠͷग़ݱසΛ༻͍ͨك᧵ͷݕग़ɼݚڀใࠂΠϯλʔ ωοτͱӡ༻ٕज़ *05 ɼWPM*05ɼQQrɼ <>ࡾ༔հɼ܀ྛ݈ଠɼඇఆৗͳଟόϯσΟοτʹ͓͚ΔมԽݕग़ΞϓϩʔνͷઢܗϞσϧͷ֦ுɼݚڀใࠂΠϯλʔωο τͱӡ༻ٕज़ *05 ɼWPM*05ɼQQrɼ+VMZ
3. ത࢜՝ఔͰͷݚڀܭը
1. ΑΓଟ͘ͷจ຺Λѻ͏ 2. ΑΓޮՌతͳఏҊΛߦ͏ 3. ΑΓૉૣ͘ڥͷมԽΛݕ͠దԠ͢Δ 29 ݱݚڀ(Synapse)ͷ feature works
1. ΑΓଟ͘ͷจ຺Λѻ͏ 2. ΑΓޮՌతͳఏҊΛߦ͏ 3. ΑΓૉૣ͘ڥͷมԽΛݕ͠దԠ͢Δ 30 ݱݚڀ(Synapse)ͷ feature works
• ത࢜՝ఔΛ௨ͯ͜͡ΕΒͷ߲Λݚڀ͠ɺਪનख๏͚ͩͰͳ༷͘ʑͳબࢶ͔ Βɺར༻ऀใγεςϜ͕ͲͷΑ͏ͳঢ়گͰ͋ͬͯ࠷దͳఏҊΛߦ͑Δɺ దԠతͳਪનγεςϜͷ࣮ݱΛࢦ͢
• ݱఏҊγεςϜͰɺঢ়ଶͷਪఆ͕ൺֱత༰қͳཁҼʢ࣌ؒͷܦաʹ͏ਪન ख๏ͷ༗ޮੑͷมಈɺӾཡதͷΧςΰϦʣΛ༻͍ͯจ຺Λಛఆ • → ΑΓଟ͘ͷจ຺Λѻ͏͜ͱͰɺจ຺ʹԠͨ͡࠷దͳఏҊʹͭͳ͍͛ͨ 31 1. ૢ࡞ཤྺ͔Βͷར༻ऀͷจ຺ਪఆ •
ར༻ऀͷߪങతߪೖҙཉͷมԽΛจ຺ͱͯ͠ѻ͏ • ใγεςϜʹ͓͍ͯ໌ࣔతʹΔ͜ͱ͕Ͱ͖ͳ͍ͨΊߦಈ͔Βਪఆ͕ඞཁ • ·ͨɺจ຺ʹԠͯ͡ఏҊΛ࠷దԽ͢ΔͨΊʹਪఆΛଈ࣌ߦ͏ඞཁ͕͋Δ
32 1. ૢ࡞ཤྺ͔Βͷར༻ऀͷจ຺ਪఆʢ༧උධՁʣ • ECαΠτͷར༻ऀͷӾཡཤྺ͔Βߪങʹͭͳ͕ΔߦಈͷมԽΛݕग़[3] • ϚʔέςΟϯάɾαΠΤϯεΦϖϨʔγϣϯɾϦαʔνʹ͓͚Δߦಈ ܾఆϞσϧΛࢀߟʹɺҰఆظؒʹӾཡͨ͠ͷछྨͷݮগΛߪങҙཉͷ ૿ՃͱΈͳ͢ •
౷ܭతԾઆݕఆΛ༻͍ͨมԽݕग़ख๏Ͱͷɺਫ਼ΛධՁ • → ΑΓޮՌతͳಛྔͱมԽݕग़ख๏ʹ͍ͭͯݚڀΛਐΊΔ <>ࡒେՆɼࡾ༔հɼ&$αΠτʹ͓͚ΔӾཡཤྺΛ༻͍ͨߪങʹܨ͕ΔߦಈͷมԽݕग़ɼݚڀใࠂΠϯλʔωοτͱӡ༻ٕज़ *05 ɼ WPM*05ɼQQrɼ
• ݱఏҊγεςϜͰɺఏҊͷ༗ޮੑʹجͮࣗ͘ಈతͳ͍͚͕ߦ͑ΔҰํɺ จ຺ͱఏҊͷؔੑෆ໌ɻ • → ΑΓޮՌతͳఏҊΛߦ͏ͨΊɺ͜ͷؔੑΛੳ͠ɺจ຺ʹಛԽͨ͠ޮ ՌతͳఏҊํࣜΛݕ౼͍ͨ͠ 33 2. ಛఆͷจ຺ʹ༗ޮͳఏҊํࣜ
• ࡞ͷ͠͞ʹͨ͠ਪનख๏ͷݕ౼[4] • ࡞ू߹ʹ͓͚Δ࡞໊ʹؚ·ΕΔ୯ޠͷग़ݱස͔Β͠͞ΛఆྔԽ • ֤୯ޠͷඪ४ภࠩ༻͍Δ͜ͱͰ༻్ผʹ͠͞Λྨ͠ਫ਼ΛධՁ • → ߪങҙཉͷԼʹରͯ͠ɺมಈΛͨΒ͢ཁҼͱͳΓ͏Δ͔ఏҊγες ϜΛ༻͍ͯධՁΛਐΊΔ
34 2. ಛఆͷจ຺ʹ༗ޮͳఏҊํࣜʢ༧උධՁʣ <>ଜ໋ɼࡾ༔հɼϋϯυϝΠυ࡞Λରͱͨ͠&$αΠτʹ͓͚Δ୯ޠͷग़ݱසΛ༻͍ͨك᧵ͷݕग़ɼݚڀใࠂΠϯλʔ ωοτͱӡ༻ٕज़ *05 ɼWPM*05ɼQQrɼ
• ݱఏҊγεςϜͰɺҎԼͷཧ༝͔Β࣌ؒͷܦաʹ͏มಈʹରͯ͠ɺैੑ ͕ॆͰͳ͔ͬͨɻ • ᶃ γεςϜߏͷ੍: ར༻ऀͷఏҊʹର͢ΔධՁͷө͕ҰఆظؒԆ • ᶄ ղ๏ͷ੍:
ैདྷͷଟόϯσΟοτͷղ๏Ͱʮଟ༷͔ͭܧଓత ʹʯมԽ͢Δڥʹॆै͢Δ͜ͱ͕Ͱ͖ͳ͍ • → ͜ΕΒΛղফ͠ɺΑΓૉૣ͘ڥͷมԽΛݕ͠దԠ͢ΔγεςϜઃܭ ͱ͍ͨ͠ 35 3. ଈ࣌ʹఏҊͷ࠷దԽΛߦ͏γεςϜઃܭ
• ᶄʹ͍ͭͯɺैདྷͷଟόϯσΟοτͷղ๏͕ݸผʹߟྀ͍ͯͨ͠จ຺ͱ ใुͷมԽʹରͯ͠ɺಉ࣌ʹରԠͰ͖ΔΑ͏ɺ͜ΕΒͷղ๏Λ֦ு[2] • ैདྷղ๏Λ൚༻తʹར༻Ͱ͖ɺใुͷมԽʹରͯ͠ྑ͍ධՁ͕ಘΒΕͯ ͍ΔɺมԽݕग़ܕΞϓϩʔνͰ͋ΔS-TS-ADWINͷ֦ு • → ୯Ұͷจ຺ͰͷใुͷมԽͰͳ͘ෳͷจ຺ͰͷมԽʹରԠ •
จ຺ͷύλʔϯ͝ͱͰͳ͘ɺਪఆͨ͠ઢܗύϥϝʔλͷ͔ΒมԽݕग़ • ୳ࡧϋΠύʔύϥϝʔλͷಈతͳௐΛಋೖɺੵۃతʹ׆༻ͱ୳ࡧΛସ 36 3. ଈ࣌ʹఏҊͷ࠷దԽΛߦ͏γεςϜઃܭʢ༧උධՁʣ <>ࡾ༔հɼ܀ྛ݈ଠɼඇఆৗͳଟόϯσΟοτʹ͓͚ΔมԽݕग़ΞϓϩʔνͷઢܗϞσϧͷ֦ுɼݚڀใࠂΠϯλʔωο τͱӡ༻ٕज़ *05 ɼWPM*05ɼQQrɼ+VMZ
εέδϡʔϧ
38 ത࢜จ·Ͱͷݚڀεέδϡʔϧ લ ޙ લ ޙ
લ ޙ લ ޙ ݱݚڀ จ຺ਪఆ จ຺ಛԽఏҊ దԠγεςϜ ത࢜จ จࢽൃද ࡁ ࠃࡍձٞൃද จࢽൃද ࠃࡍձٞൃද จࢽൃද ത࢜จʙެௌձ αʔϕΠɾ༧උධՁ ༧උධՁ ࡁ
None