Suche senden
Hochladen
SHOGUN使ってみました
•
9 gefällt mir
•
5,951 views
Yasutomo Kawanishi
Folgen
第1回:はじめてのSHOGUN編
Weniger lesen
Mehr lesen
Technologie
Melden
Teilen
Melden
Teilen
1 von 80
Empfohlen
Java 8 고급 (6/6)
Java 8 고급 (6/6)
Kyung Koo Yoon
curl manual
curl manual
Anthony Yuan , PMP
Deploy, Scale and Sleep at Night with JRuby
Deploy, Scale and Sleep at Night with JRuby
Joe Kutner
002 hbase clientapi
002 hbase clientapi
Scott Miao
005 cluster monitoring
005 cluster monitoring
Scott Miao
When Ruby Meets Java - The Power of Torquebox
When Ruby Meets Java - The Power of Torquebox
rockyjaiswal
003 admin featuresandclients
003 admin featuresandclients
Scott Miao
Curl
Curl
manasystest
Empfohlen
Java 8 고급 (6/6)
Java 8 고급 (6/6)
Kyung Koo Yoon
curl manual
curl manual
Anthony Yuan , PMP
Deploy, Scale and Sleep at Night with JRuby
Deploy, Scale and Sleep at Night with JRuby
Joe Kutner
002 hbase clientapi
002 hbase clientapi
Scott Miao
005 cluster monitoring
005 cluster monitoring
Scott Miao
When Ruby Meets Java - The Power of Torquebox
When Ruby Meets Java - The Power of Torquebox
rockyjaiswal
003 admin featuresandclients
003 admin featuresandclients
Scott Miao
Curl
Curl
manasystest
Programming using Open Mp
Programming using Open Mp
Anshul Sharma
TorqueBox at DC:JBUG - November 2011
TorqueBox at DC:JBUG - November 2011
bobmcwhirter
Ffmpeg
Ffmpeg
duquoi
OpenStack Swift Command Line Reference Diablo v1.2
OpenStack Swift Command Line Reference Diablo v1.2
Amar Kapadia
Find bottleneck and tuning in Java Application
Find bottleneck and tuning in Java Application
guest1f2740
DataMapper on Infinispan
DataMapper on Infinispan
Lance Ball
RubyGems 3 & 4
RubyGems 3 & 4
Hiroshi SHIBATA
Complex Made Simple: Sleep Better with TorqueBox
Complex Made Simple: Sleep Better with TorqueBox
bobmcwhirter
Tips of Malloc & Free
Tips of Malloc & Free
Tetsuyuki Kobayashi
TorqueBox - Ruby Hoedown 2011
TorqueBox - Ruby Hoedown 2011
Lance Ball
ZK_Arch_notes_20081121
ZK_Arch_notes_20081121
WANGCHOU LU
Ruby 2.4 Internals
Ruby 2.4 Internals
Koichi Sasada
20140419 oedo rubykaigi04
20140419 oedo rubykaigi04
Hiroshi SHIBATA
Lec7
Lec7
Heather Kulik
Configuration management II - Terraform
Configuration management II - Terraform
Xavier Serrat Bordas
How to distribute Ruby to the world
How to distribute Ruby to the world
Hiroshi SHIBATA
20140925 rails pacific
20140925 rails pacific
Hiroshi SHIBATA
RubyGems 3 & 4
RubyGems 3 & 4
Hiroshi SHIBATA
20140425 ruby conftaiwan2014
20140425 ruby conftaiwan2014
Hiroshi SHIBATA
JVM Internals (2015)
JVM Internals (2015)
Luiz Fernando Teston
Jvm internals
Jvm internals
Luiz Fernando Teston
Jvm Performance Tunning
Jvm Performance Tunning
guest1f2740
Weitere ähnliche Inhalte
Was ist angesagt?
Programming using Open Mp
Programming using Open Mp
Anshul Sharma
TorqueBox at DC:JBUG - November 2011
TorqueBox at DC:JBUG - November 2011
bobmcwhirter
Ffmpeg
Ffmpeg
duquoi
OpenStack Swift Command Line Reference Diablo v1.2
OpenStack Swift Command Line Reference Diablo v1.2
Amar Kapadia
Find bottleneck and tuning in Java Application
Find bottleneck and tuning in Java Application
guest1f2740
DataMapper on Infinispan
DataMapper on Infinispan
Lance Ball
RubyGems 3 & 4
RubyGems 3 & 4
Hiroshi SHIBATA
Complex Made Simple: Sleep Better with TorqueBox
Complex Made Simple: Sleep Better with TorqueBox
bobmcwhirter
Tips of Malloc & Free
Tips of Malloc & Free
Tetsuyuki Kobayashi
TorqueBox - Ruby Hoedown 2011
TorqueBox - Ruby Hoedown 2011
Lance Ball
ZK_Arch_notes_20081121
ZK_Arch_notes_20081121
WANGCHOU LU
Ruby 2.4 Internals
Ruby 2.4 Internals
Koichi Sasada
20140419 oedo rubykaigi04
20140419 oedo rubykaigi04
Hiroshi SHIBATA
Lec7
Lec7
Heather Kulik
Configuration management II - Terraform
Configuration management II - Terraform
Xavier Serrat Bordas
How to distribute Ruby to the world
How to distribute Ruby to the world
Hiroshi SHIBATA
20140925 rails pacific
20140925 rails pacific
Hiroshi SHIBATA
RubyGems 3 & 4
RubyGems 3 & 4
Hiroshi SHIBATA
20140425 ruby conftaiwan2014
20140425 ruby conftaiwan2014
Hiroshi SHIBATA
JVM Internals (2015)
JVM Internals (2015)
Luiz Fernando Teston
Was ist angesagt?
(20)
Programming using Open Mp
Programming using Open Mp
TorqueBox at DC:JBUG - November 2011
TorqueBox at DC:JBUG - November 2011
Ffmpeg
Ffmpeg
OpenStack Swift Command Line Reference Diablo v1.2
OpenStack Swift Command Line Reference Diablo v1.2
Find bottleneck and tuning in Java Application
Find bottleneck and tuning in Java Application
DataMapper on Infinispan
DataMapper on Infinispan
RubyGems 3 & 4
RubyGems 3 & 4
Complex Made Simple: Sleep Better with TorqueBox
Complex Made Simple: Sleep Better with TorqueBox
Tips of Malloc & Free
Tips of Malloc & Free
TorqueBox - Ruby Hoedown 2011
TorqueBox - Ruby Hoedown 2011
ZK_Arch_notes_20081121
ZK_Arch_notes_20081121
Ruby 2.4 Internals
Ruby 2.4 Internals
20140419 oedo rubykaigi04
20140419 oedo rubykaigi04
Lec7
Lec7
Configuration management II - Terraform
Configuration management II - Terraform
How to distribute Ruby to the world
How to distribute Ruby to the world
20140925 rails pacific
20140925 rails pacific
RubyGems 3 & 4
RubyGems 3 & 4
20140425 ruby conftaiwan2014
20140425 ruby conftaiwan2014
JVM Internals (2015)
JVM Internals (2015)
Ähnlich wie SHOGUN使ってみました
Jvm internals
Jvm internals
Luiz Fernando Teston
Jvm Performance Tunning
Jvm Performance Tunning
guest1f2740
Jvm Performance Tunning
Jvm Performance Tunning
Terry Cho
Tutorial - Support vector machines
Tutorial - Support vector machines
butest
Tutorial - Support vector machines
Tutorial - Support vector machines
butest
Beirut Java User Group JVM presentation
Beirut Java User Group JVM presentation
Mahmoud Anouti
Новый InterSystems: open-source, митапы, хакатоны
Новый InterSystems: open-source, митапы, хакатоны
Timur Safin
Software Profiling: Understanding Java Performance and how to profile in Java
Software Profiling: Understanding Java Performance and how to profile in Java
Isuru Perera
Introduction to LAVA Workload Scheduler
Introduction to LAVA Workload Scheduler
Nopparat Nopkuat
New features in Ruby 2.5
New features in Ruby 2.5
Ireneusz Skrobiś
Artimon - Apache Flume (incubating) NYC Meetup 20111108
Artimon - Apache Flume (incubating) NYC Meetup 20111108
Mathias Herberts
Deep Learning for Computer Vision: Software Frameworks (UPC 2016)
Deep Learning for Computer Vision: Software Frameworks (UPC 2016)
Universitat Politècnica de Catalunya
Meder Kydyraliev - Mining Mach Services within OS X Sandbox
Meder Kydyraliev - Mining Mach Services within OS X Sandbox
DefconRussia
Grow and Shrink - Dynamically Extending the Ruby VM Stack
Grow and Shrink - Dynamically Extending the Ruby VM Stack
KeitaSugiyama1
Adopting GraalVM - Scale by the Bay 2018
Adopting GraalVM - Scale by the Bay 2018
Petr Zapletal
Python + GDB = Javaデバッガ
Python + GDB = Javaデバッガ
Kenji Kazumura
DevOps(4) : Ansible(2) - (MOSG)
DevOps(4) : Ansible(2) - (MOSG)
Soshi Nemoto
[BGOUG] Java GC - Friend or Foe
[BGOUG] Java GC - Friend or Foe
SAP HANA Cloud Platform
Adding a BOLT pass
Adding a BOLT pass
Amir42407
Quantifying Container Runtime Performance: OSCON 2017 Open Container Day
Quantifying Container Runtime Performance: OSCON 2017 Open Container Day
Phil Estes
Ähnlich wie SHOGUN使ってみました
(20)
Jvm internals
Jvm internals
Jvm Performance Tunning
Jvm Performance Tunning
Jvm Performance Tunning
Jvm Performance Tunning
Tutorial - Support vector machines
Tutorial - Support vector machines
Tutorial - Support vector machines
Tutorial - Support vector machines
Beirut Java User Group JVM presentation
Beirut Java User Group JVM presentation
Новый InterSystems: open-source, митапы, хакатоны
Новый InterSystems: open-source, митапы, хакатоны
Software Profiling: Understanding Java Performance and how to profile in Java
Software Profiling: Understanding Java Performance and how to profile in Java
Introduction to LAVA Workload Scheduler
Introduction to LAVA Workload Scheduler
New features in Ruby 2.5
New features in Ruby 2.5
Artimon - Apache Flume (incubating) NYC Meetup 20111108
Artimon - Apache Flume (incubating) NYC Meetup 20111108
Deep Learning for Computer Vision: Software Frameworks (UPC 2016)
Deep Learning for Computer Vision: Software Frameworks (UPC 2016)
Meder Kydyraliev - Mining Mach Services within OS X Sandbox
Meder Kydyraliev - Mining Mach Services within OS X Sandbox
Grow and Shrink - Dynamically Extending the Ruby VM Stack
Grow and Shrink - Dynamically Extending the Ruby VM Stack
Adopting GraalVM - Scale by the Bay 2018
Adopting GraalVM - Scale by the Bay 2018
Python + GDB = Javaデバッガ
Python + GDB = Javaデバッガ
DevOps(4) : Ansible(2) - (MOSG)
DevOps(4) : Ansible(2) - (MOSG)
[BGOUG] Java GC - Friend or Foe
[BGOUG] Java GC - Friend or Foe
Adding a BOLT pass
Adding a BOLT pass
Quantifying Container Runtime Performance: OSCON 2017 Open Container Day
Quantifying Container Runtime Performance: OSCON 2017 Open Container Day
Mehr von Yasutomo Kawanishi
TransPose: Towards Explainable Human Pose Estimation by Transformer
TransPose: Towards Explainable Human Pose Estimation by Transformer
Yasutomo Kawanishi
全日本コンピュータビジョン勉強会:Disentangling and Unifying Graph Convolutions for Skeleton-B...
全日本コンピュータビジョン勉強会:Disentangling and Unifying Graph Convolutions for Skeleton-B...
Yasutomo Kawanishi
Pythonによる機械学習入門 ~Deep Learningに挑戦~
Pythonによる機械学習入門 ~Deep Learningに挑戦~
Yasutomo Kawanishi
Pythonによる機械学習入門 ~SVMからDeep Learningまで~
Pythonによる機械学習入門 ~SVMからDeep Learningまで~
Yasutomo Kawanishi
Pythonによる機械学習入門〜基礎からDeep Learningまで〜
Pythonによる機械学習入門〜基礎からDeep Learningまで〜
Yasutomo Kawanishi
サーベイ論文:画像からの歩行者属性認識
サーベイ論文:画像からの歩行者属性認識
Yasutomo Kawanishi
Pythonによる画像処理について
Pythonによる画像処理について
Yasutomo Kawanishi
ACCV2014参加報告
ACCV2014参加報告
Yasutomo Kawanishi
背景モデリングに関する研究など
背景モデリングに関する研究など
Yasutomo Kawanishi
画像処理でのPythonの利用
画像処理でのPythonの利用
Yasutomo Kawanishi
第17回関西CVPRML勉強会 (一般物体認識) 1,2節
第17回関西CVPRML勉強会 (一般物体認識) 1,2節
Yasutomo Kawanishi
SNSでひろがるプライバシ制御センシング
SNSでひろがるプライバシ制御センシング
Yasutomo Kawanishi
Mehr von Yasutomo Kawanishi
(12)
TransPose: Towards Explainable Human Pose Estimation by Transformer
TransPose: Towards Explainable Human Pose Estimation by Transformer
全日本コンピュータビジョン勉強会:Disentangling and Unifying Graph Convolutions for Skeleton-B...
全日本コンピュータビジョン勉強会:Disentangling and Unifying Graph Convolutions for Skeleton-B...
Pythonによる機械学習入門 ~Deep Learningに挑戦~
Pythonによる機械学習入門 ~Deep Learningに挑戦~
Pythonによる機械学習入門 ~SVMからDeep Learningまで~
Pythonによる機械学習入門 ~SVMからDeep Learningまで~
Pythonによる機械学習入門〜基礎からDeep Learningまで〜
Pythonによる機械学習入門〜基礎からDeep Learningまで〜
サーベイ論文:画像からの歩行者属性認識
サーベイ論文:画像からの歩行者属性認識
Pythonによる画像処理について
Pythonによる画像処理について
ACCV2014参加報告
ACCV2014参加報告
背景モデリングに関する研究など
背景モデリングに関する研究など
画像処理でのPythonの利用
画像処理でのPythonの利用
第17回関西CVPRML勉強会 (一般物体認識) 1,2節
第17回関西CVPRML勉強会 (一般物体認識) 1,2節
SNSでひろがるプライバシ制御センシング
SNSでひろがるプライバシ制御センシング
Kürzlich hochgeladen
React Native vs Ionic - The Best Mobile App Framework
React Native vs Ionic - The Best Mobile App Framework
Pixlogix Infotech
New from BookNet Canada for 2024: Loan Stars - Tech Forum 2024
New from BookNet Canada for 2024: Loan Stars - Tech Forum 2024
BookNet Canada
Merck Moving Beyond Passwords: FIDO Paris Seminar.pptx
Merck Moving Beyond Passwords: FIDO Paris Seminar.pptx
LoriGlavin3
A Journey Into the Emotions of Software Developers
A Journey Into the Emotions of Software Developers
Nicole Novielli
How to write a Business Continuity Plan
How to write a Business Continuity Plan
Databarracks
The Role of FIDO in a Cyber Secure Netherlands: FIDO Paris Seminar.pptx
The Role of FIDO in a Cyber Secure Netherlands: FIDO Paris Seminar.pptx
LoriGlavin3
The Ultimate Guide to Choosing WordPress Pros and Cons
The Ultimate Guide to Choosing WordPress Pros and Cons
Pixlogix Infotech
Zeshan Sattar- Assessing the skill requirements and industry expectations for...
Zeshan Sattar- Assessing the skill requirements and industry expectations for...
itnewsafrica
Genislab builds better products and faster go-to-market with Lean project man...
Genislab builds better products and faster go-to-market with Lean project man...
Farhan Tariq
Moving Beyond Passwords: FIDO Paris Seminar.pdf
Moving Beyond Passwords: FIDO Paris Seminar.pdf
LoriGlavin3
MuleSoft Online Meetup Group - B2B Crash Course: Release SparkNotes
MuleSoft Online Meetup Group - B2B Crash Course: Release SparkNotes
Manik S Magar
So einfach geht modernes Roaming fuer Notes und Nomad.pdf
So einfach geht modernes Roaming fuer Notes und Nomad.pdf
panagenda
The State of Passkeys with FIDO Alliance.pptx
The State of Passkeys with FIDO Alliance.pptx
LoriGlavin3
Passkey Providers and Enabling Portability: FIDO Paris Seminar.pptx
Passkey Providers and Enabling Portability: FIDO Paris Seminar.pptx
LoriGlavin3
Unleashing Real-time Insights with ClickHouse_ Navigating the Landscape in 20...
Unleashing Real-time Insights with ClickHouse_ Navigating the Landscape in 20...
Alkin Tezuysal
QCon London: Mastering long-running processes in modern architectures
QCon London: Mastering long-running processes in modern architectures
Bernd Ruecker
Connecting the Dots for Information Discovery.pdf
Connecting the Dots for Information Discovery.pdf
Neo4j
Bridging Between CAD & GIS: 6 Ways to Automate Your Data Integration
Bridging Between CAD & GIS: 6 Ways to Automate Your Data Integration
marketing932765
Potential of AI (Generative AI) in Business: Learnings and Insights
Potential of AI (Generative AI) in Business: Learnings and Insights
Ravi Sanghani
A Framework for Development in the AI Age
A Framework for Development in the AI Age
Cprime
Kürzlich hochgeladen
(20)
React Native vs Ionic - The Best Mobile App Framework
React Native vs Ionic - The Best Mobile App Framework
New from BookNet Canada for 2024: Loan Stars - Tech Forum 2024
New from BookNet Canada for 2024: Loan Stars - Tech Forum 2024
Merck Moving Beyond Passwords: FIDO Paris Seminar.pptx
Merck Moving Beyond Passwords: FIDO Paris Seminar.pptx
A Journey Into the Emotions of Software Developers
A Journey Into the Emotions of Software Developers
How to write a Business Continuity Plan
How to write a Business Continuity Plan
The Role of FIDO in a Cyber Secure Netherlands: FIDO Paris Seminar.pptx
The Role of FIDO in a Cyber Secure Netherlands: FIDO Paris Seminar.pptx
The Ultimate Guide to Choosing WordPress Pros and Cons
The Ultimate Guide to Choosing WordPress Pros and Cons
Zeshan Sattar- Assessing the skill requirements and industry expectations for...
Zeshan Sattar- Assessing the skill requirements and industry expectations for...
Genislab builds better products and faster go-to-market with Lean project man...
Genislab builds better products and faster go-to-market with Lean project man...
Moving Beyond Passwords: FIDO Paris Seminar.pdf
Moving Beyond Passwords: FIDO Paris Seminar.pdf
MuleSoft Online Meetup Group - B2B Crash Course: Release SparkNotes
MuleSoft Online Meetup Group - B2B Crash Course: Release SparkNotes
So einfach geht modernes Roaming fuer Notes und Nomad.pdf
So einfach geht modernes Roaming fuer Notes und Nomad.pdf
The State of Passkeys with FIDO Alliance.pptx
The State of Passkeys with FIDO Alliance.pptx
Passkey Providers and Enabling Portability: FIDO Paris Seminar.pptx
Passkey Providers and Enabling Portability: FIDO Paris Seminar.pptx
Unleashing Real-time Insights with ClickHouse_ Navigating the Landscape in 20...
Unleashing Real-time Insights with ClickHouse_ Navigating the Landscape in 20...
QCon London: Mastering long-running processes in modern architectures
QCon London: Mastering long-running processes in modern architectures
Connecting the Dots for Information Discovery.pdf
Connecting the Dots for Information Discovery.pdf
Bridging Between CAD & GIS: 6 Ways to Automate Your Data Integration
Bridging Between CAD & GIS: 6 Ways to Automate Your Data Integration
Potential of AI (Generative AI) in Business: Learnings and Insights
Potential of AI (Generative AI) in Business: Learnings and Insights
A Framework for Development in the AI Age
A Framework for Development in the AI Age
SHOGUN使ってみました
1.
SHOGUN 2011 4
23 9 CV PRML @yasutomo57jp ( @inco_san )
2.
SHOGUN
1 SHOGUN 2011 4 23 9 CV PRML @yasutomo57jp ( @inco_san )
3.
SHOGUN
4.
* OpenCV * http://d.hatena.ne.jp/takmin/20110306/1299423617
5.
6.
7.
• SHOGUN
8.
• SHOGUN • 1
SHOGUN
9.
• SHOGUN • 1
SHOGUN • Static Interface
10.
• SHOGUN • 1
SHOGUN • Static Interface • 2 SHOGUN
11.
• SHOGUN • 1
SHOGUN • Static Interface • 2 SHOGUN • Modular Interface
12.
• SHOGUN • 1
SHOGUN • Static Interface • 2 SHOGUN • Modular Interface • 3 C++ ( )
13.
• SHOGUN • 1
SHOGUN • Static Interface • 2 SHOGUN • Modular Interface • 3 C++ ( ) • libshogun
14.
• SHOGUN • 1
SHOGUN • Static Interface • 2 SHOGUN • Modular Interface • 3 C++ ( ) • libshogun
15.
• SHOGUN • 1
SHOGUN • Static Interface • 2 SHOGUN • Modular Interface • 3 C++ ( ) • libshogun
16.
SHOGUN
17.
SHOGUN •
18.
SHOGUN •
• SVM !
19.
SHOGUN •
• SVM ! • SVM OCAS, Liblinear, LibSVM, SVMLight, SVMLin, GPDT
20.
SHOGUN •
• SVM ! • SVM OCAS, Liblinear, LibSVM, SVMLight, SVMLin, GPDT • Linear, Polynomial, Gaussian and Sigmoid Kernel
21.
SHOGUN •
• SVM ! • SVM OCAS, Liblinear, LibSVM, SVMLight, SVMLin, GPDT • Linear, Polynomial, Gaussian and Sigmoid Kernel •
22.
SHOGUN • SVM
!! • LDA : Linear Discriminant Analysis • LPM : Linear Programming Machine • (Kernel) Perceptron • HMM
23.
SHOGUN • •
24.
SHOGUN
25.
Q. Matlab
26.
27.
Q. Matlab
28.
Octave
29.
Python
30.
Python
31.
Q. C++
…
32.
…
33.
…
34.
SHOGUN
35.
SHOGUN
36.
37.
38.
39.
40.
• Static Interface
• • • Modular Interface • Python Octave • • libshogun • C++ •
41.
• Static Interface
• • • Modular Interface • Python Octave • • libshogun • C++ •
42.
43.
44.
Windows Cygwin http://www.shogun-toolbox.org/#releases
45.
Windows
Linux (Ubuntu) Cygwin sudo apt-get install shogun http://www.shogun-toolbox.org/#releases
46.
Windows
Linux (Ubuntu) Cygwin sudo apt-get install shogun http://www.shogun-toolbox.org/#releases Mac sudo port install shogun
47.
Windows
Linux (Ubuntu) Cygwin sudo apt-get install shogun http://www.shogun-toolbox.org/#releases Mac sudo port install shogun OK
48.
49.
SVM
•• libsvm (Cmdline ) set_kernel GAUSSIAN REAL 10 1.2 set_features TRAIN ../data/fm_train_real.dat set_labels TRAIN ../data/label_train_twoclass.dat new_classifier LIBSVM c1 train_classifier save_classifier libsvm.model load_classifier libsvm.model LIBSVM set_features TEST ../data/fm_test_real.dat out.txt=classify
50.
SVM
•• libsvm (Cmdline ) set_kernel GAUSSIAN REAL 10 1.2 (cache, kernel width) set_features TRAIN ../data/fm_train_real.dat set_labels TRAIN ../data/label_train_twoclass.dat new_classifier LIBSVM c1 train_classifier save_classifier libsvm.model load_classifier libsvm.model LIBSVM set_features TEST ../data/fm_test_real.dat out.txt=classify
51.
SVM
•• libsvm (Cmdline ) set_kernel GAUSSIAN REAL 10 1.2 (cache, kernel width) set_features TRAIN ../data/fm_train_real.dat set_labels TRAIN ../data/label_train_twoclass.dat new_classifier LIBSVM c1 train_classifier save_classifier libsvm.model load_classifier libsvm.model LIBSVM set_features TEST ../data/fm_test_real.dat out.txt=classify
52.
SVM
•• libsvm (Cmdline ) set_kernel GAUSSIAN REAL 10 1.2 (cache, kernel width) set_features TRAIN ../data/fm_train_real.dat set_labels TRAIN ../data/label_train_twoclass.dat new_classifier LIBSVM c1 train_classifier save_classifier libsvm.model load_classifier libsvm.model LIBSVM set_features TEST ../data/fm_test_real.dat out.txt=classify
53.
SVM
•• libsvm (Cmdline ) set_kernel GAUSSIAN REAL 10 1.2 (cache, kernel width) set_features TRAIN ../data/fm_train_real.dat set_labels TRAIN ../data/label_train_twoclass.dat new_classifier LIBSVM libsvm c1 train_classifier save_classifier libsvm.model load_classifier libsvm.model LIBSVM set_features TEST ../data/fm_test_real.dat out.txt=classify
54.
SVM
•• libsvm (Cmdline ) set_kernel GAUSSIAN REAL 10 1.2 (cache, kernel width) set_features TRAIN ../data/fm_train_real.dat set_labels TRAIN ../data/label_train_twoclass.dat new_classifier LIBSVM libsvm c1 C 1 train_classifier save_classifier libsvm.model load_classifier libsvm.model LIBSVM set_features TEST ../data/fm_test_real.dat out.txt=classify
55.
SVM
•• libsvm (Cmdline ) set_kernel GAUSSIAN REAL 10 1.2 (cache, kernel width) set_features TRAIN ../data/fm_train_real.dat set_labels TRAIN ../data/label_train_twoclass.dat new_classifier LIBSVM libsvm c1 C 1 train_classifier SVM save_classifier libsvm.model load_classifier libsvm.model LIBSVM set_features TEST ../data/fm_test_real.dat out.txt=classify
56.
SVM
•• libsvm (Cmdline ) set_kernel GAUSSIAN REAL 10 1.2 (cache, kernel width) set_features TRAIN ../data/fm_train_real.dat set_labels TRAIN ../data/label_train_twoclass.dat new_classifier LIBSVM libsvm c1 C 1 train_classifier SVM save_classifier libsvm.model load_classifier libsvm.model LIBSVM set_features TEST ../data/fm_test_real.dat out.txt=classify
57.
SVM
•• libsvm (Cmdline ) set_kernel GAUSSIAN REAL 10 1.2 (cache, kernel width) set_features TRAIN ../data/fm_train_real.dat set_labels TRAIN ../data/label_train_twoclass.dat new_classifier LIBSVM libsvm c1 C 1 train_classifier SVM save_classifier libsvm.model load_classifier libsvm.model LIBSVM set_features TEST ../data/fm_test_real.dat out.txt=classify
58.
SVM
•• libsvm (Cmdline ) set_kernel GAUSSIAN REAL 10 1.2 (cache, kernel width) set_features TRAIN ../data/fm_train_real.dat set_labels TRAIN ../data/label_train_twoclass.dat new_classifier LIBSVM libsvm c1 C 1 train_classifier SVM save_classifier libsvm.model load_classifier libsvm.model LIBSVM set_features TEST ../data/fm_test_real.dat out.txt=classify
59.
SVM
•• libsvm (Cmdline ) set_kernel GAUSSIAN REAL 10 1.2 (cache, kernel width) set_features TRAIN ../data/fm_train_real.dat set_labels TRAIN ../data/label_train_twoclass.dat new_classifier LIBSVM libsvm c1 C 1 train_classifier SVM save_classifier libsvm.model load_classifier libsvm.model LIBSVM set_features TEST ../data/fm_test_real.dat out.txt=classify out.txt
60.
••
libsvm (Cmdline ) set_kernel SIGMOID REAL 50 3 0 (cache, gamma, coeff) set_features TRAIN ../data/fm_train_real.dat set_labels TRAIN ../data/label_train_twoclass.dat new_classifier LIBSVM libsvm c1 C 1 train_classifier SVM save_classifier libsvm.model load_classifier libsvm.model LIBSVM set_features TEST ../data/fm_test_real.dat out.txt=classify out.txt
61.
SVM
•• svmlight (Cmdline ) set_kernel GAUSSIAN REAL 10 1.2 set_features TRAIN ../data/fm_train_real.dat set_labels TRAIN ../data/label_train_twoclass.dat new_classifier LIGHT c1 train_classifier save_classifier libsvm.model load_classifier libsvm.model LIBSVM set_features TEST ../data/fm_test_real.dat out.txt=classify
62.
SVM
•• svmlight (Cmdline ) set_kernel GAUSSIAN REAL 10 1.2 (cache, kernel width) set_features TRAIN ../data/fm_train_real.dat set_labels TRAIN ../data/label_train_twoclass.dat new_classifier LIGHT c1 train_classifier save_classifier libsvm.model load_classifier libsvm.model LIBSVM set_features TEST ../data/fm_test_real.dat out.txt=classify
63.
SVM
•• svmlight (Cmdline ) set_kernel GAUSSIAN REAL 10 1.2 (cache, kernel width) set_features TRAIN ../data/fm_train_real.dat set_labels TRAIN ../data/label_train_twoclass.dat new_classifier LIGHT c1 train_classifier save_classifier libsvm.model load_classifier libsvm.model LIBSVM set_features TEST ../data/fm_test_real.dat out.txt=classify
64.
SVM
•• svmlight (Cmdline ) set_kernel GAUSSIAN REAL 10 1.2 (cache, kernel width) set_features TRAIN ../data/fm_train_real.dat set_labels TRAIN ../data/label_train_twoclass.dat new_classifier LIGHT c1 train_classifier save_classifier libsvm.model load_classifier libsvm.model LIBSVM set_features TEST ../data/fm_test_real.dat out.txt=classify
65.
SVM
•• svmlight (Cmdline ) set_kernel GAUSSIAN REAL 10 1.2 (cache, kernel width) set_features TRAIN ../data/fm_train_real.dat set_labels TRAIN ../data/label_train_twoclass.dat new_classifier LIGHT libsvm c1 train_classifier save_classifier libsvm.model load_classifier libsvm.model LIBSVM set_features TEST ../data/fm_test_real.dat out.txt=classify
66.
SVM
•• svmlight (Cmdline ) set_kernel GAUSSIAN REAL 10 1.2 (cache, kernel width) set_features TRAIN ../data/fm_train_real.dat set_labels TRAIN ../data/label_train_twoclass.dat new_classifier LIGHT libsvm c1 C 1 train_classifier save_classifier libsvm.model load_classifier libsvm.model LIBSVM set_features TEST ../data/fm_test_real.dat out.txt=classify
67.
SVM
•• svmlight (Cmdline ) set_kernel GAUSSIAN REAL 10 1.2 (cache, kernel width) set_features TRAIN ../data/fm_train_real.dat set_labels TRAIN ../data/label_train_twoclass.dat new_classifier LIGHT libsvm c1 C 1 train_classifier SVM save_classifier libsvm.model load_classifier libsvm.model LIBSVM set_features TEST ../data/fm_test_real.dat out.txt=classify
68.
SVM
•• svmlight (Cmdline ) set_kernel GAUSSIAN REAL 10 1.2 (cache, kernel width) set_features TRAIN ../data/fm_train_real.dat set_labels TRAIN ../data/label_train_twoclass.dat new_classifier LIGHT libsvm c1 C 1 train_classifier SVM save_classifier libsvm.model load_classifier libsvm.model LIBSVM set_features TEST ../data/fm_test_real.dat out.txt=classify
69.
SVM
•• svmlight (Cmdline ) set_kernel GAUSSIAN REAL 10 1.2 (cache, kernel width) set_features TRAIN ../data/fm_train_real.dat set_labels TRAIN ../data/label_train_twoclass.dat new_classifier LIGHT libsvm c1 C 1 train_classifier SVM save_classifier libsvm.model load_classifier libsvm.model LIBSVM set_features TEST ../data/fm_test_real.dat out.txt=classify
70.
SVM
•• svmlight (Cmdline ) set_kernel GAUSSIAN REAL 10 1.2 (cache, kernel width) set_features TRAIN ../data/fm_train_real.dat set_labels TRAIN ../data/label_train_twoclass.dat new_classifier LIGHT libsvm c1 C 1 train_classifier SVM save_classifier libsvm.model load_classifier libsvm.model LIBSVM set_features TEST ../data/fm_test_real.dat out.txt=classify
71.
SVM
•• svmlight (Cmdline ) set_kernel GAUSSIAN REAL 10 1.2 (cache, kernel width) set_features TRAIN ../data/fm_train_real.dat set_labels TRAIN ../data/label_train_twoclass.dat new_classifier LIGHT libsvm c1 C 1 train_classifier SVM save_classifier libsvm.model load_classifier libsvm.model LIBSVM set_features TEST ../data/fm_test_real.dat out.txt=classify out.txt
72.
Python • sg
( from sg import sg ) • sg OK • Cmdline set_feature TEST data.dat • Python sg(‘set_feature’, ‘TEST’, ‘data.dat’) http://www.shogun-toolbox.org/doc/static_tutorial.html
73.
74.
• SHOGUN
75.
• SHOGUN •3
76.
• SHOGUN •3 •
Static Interface,Modular Interface, libshogun
77.
• SHOGUN •3 •
Static Interface,Modular Interface, libshogun • Static Interface
78.
• SHOGUN •3
• Static Interface,Modular Interface, libshogun • Static Interface •
79.
• SHOGUN •3
• Static Interface,Modular Interface, libshogun • Static Interface •
80.
• SHOGUN •3
• Static Interface,Modular Interface, libshogun • Static Interface • Modular Interface
Hinweis der Redaktion
\n
\n
\n
\n
\n
\n
\n
\n
\n
\n
\n
\n
\n
\n
\n
\n
\n
\n
\n
\n
\n
\n
\n
\n
\n
\n
\n
\n
\n
\n
\n
\n
\n
\n
\n
\n
\n
\n
\n
\n
\n
\n
\n
\n
\n
\n
\n
\n
\n
\n
\n
\n
\n
\n
\n
\n
\n
\n
\n
\n
\n
\n
\n
\n
\n
\n
\n
\n
\n
\n
\n
\n
\n
\n
\n
\n
\n
\n