SlideShare une entreprise Scribd logo
1  sur  9
Experiment Result for Pair Method 2009/08/06Chi-I Kuan 1
Examinationflow 2 Combine Files  as Training Data XMLFeature Analyzer Training Xml File1 Representation XMLFeature Analyzer Training Xml File2 Representation libSVM Classifier Testing Xml File1 XMLFeature Analyzer Combine Filesas Training Data Representation Model XMLFeature Analyzer Testing Xml File2 Representation Decide Mapping
Training and Testing Data Combine two analyzed xml files as input. We has 6 bookstores’ xml file , each time we choose 2 xml file as testing data and  use the remaining 4 xml files as training data. 3
XMLAnalyzer IsDate IsTime IsFile IsHyperLink IsOnlyText IsNotText IsMixText HasOnlyDigit HasMeasurement HasUpperWord LastIsDate LastIsTime LastIsFile LastIsHyperLink LastIsOnlyText LastIsNotText LastIsMixText LastHasOnlyDigit LastHasMeasurement LastHasUpperWord 4 ,[object Object],[object Object]
Combine Combine two xml analyzed files as a pair. 6
Classifier Use libSVM to predict whether they are matching or not. 7
Decide Mapping Check classifier’s prediction result , if the vector is prediction “Match” ,then output the vector’s attribute number as result.  8 output
Examination  9
Progress Report090806

Contenu connexe

Tendances

Eclipse Memory Analyzer - More Than Just a Heap Walker
Eclipse Memory Analyzer - More Than Just a Heap WalkerEclipse Memory Analyzer - More Than Just a Heap Walker
Eclipse Memory Analyzer - More Than Just a Heap Walker
guest62fd60c
 
2009 Dils Flyweb
2009 Dils Flyweb2009 Dils Flyweb
2009 Dils Flyweb
Jun Zhao
 

Tendances (16)

Mule memory leak issue
Mule memory leak issueMule memory leak issue
Mule memory leak issue
 
OOP, Networking, Linux/Unix
OOP, Networking, Linux/UnixOOP, Networking, Linux/Unix
OOP, Networking, Linux/Unix
 
An introduction to java programming
An introduction to java programmingAn introduction to java programming
An introduction to java programming
 
Natcatchpoleslides
NatcatchpoleslidesNatcatchpoleslides
Natcatchpoleslides
 
Eclipse Memory Analyzer - More Than Just a Heap Walker
Eclipse Memory Analyzer - More Than Just a Heap WalkerEclipse Memory Analyzer - More Than Just a Heap Walker
Eclipse Memory Analyzer - More Than Just a Heap Walker
 
Method overloading
Method overloadingMethod overloading
Method overloading
 
DevNexus 2018: Learn Java 8, lambdas and functional programming
DevNexus 2018: Learn Java 8, lambdas and functional programmingDevNexus 2018: Learn Java 8, lambdas and functional programming
DevNexus 2018: Learn Java 8, lambdas and functional programming
 
Java
JavaJava
Java
 
Python with data Sciences
Python with data SciencesPython with data Sciences
Python with data Sciences
 
9 Inputs & Outputs
9 Inputs & Outputs9 Inputs & Outputs
9 Inputs & Outputs
 
Java - File Input Output Concepts
Java - File Input Output ConceptsJava - File Input Output Concepts
Java - File Input Output Concepts
 
Applications of data structures
Applications of data structuresApplications of data structures
Applications of data structures
 
Data structures and algorithms
Data structures and algorithmsData structures and algorithms
Data structures and algorithms
 
Files in java
Files in javaFiles in java
Files in java
 
Java file
Java fileJava file
Java file
 
2009 Dils Flyweb
2009 Dils Flyweb2009 Dils Flyweb
2009 Dils Flyweb
 

En vedette

Data Selection For Support Vector Machine Classifier
Data Selection For Support Vector Machine ClassifierData Selection For Support Vector Machine Classifier
Data Selection For Support Vector Machine Classifier
GUANBO
 
Basic Mapping
Basic MappingBasic Mapping
Basic Mapping
GUANBO
 
Web Information Extraction Learning based on Probabilistic Graphical Models
Web Information Extraction Learning based on Probabilistic Graphical ModelsWeb Information Extraction Learning based on Probabilistic Graphical Models
Web Information Extraction Learning based on Probabilistic Graphical Models
GUANBO
 
Information Extraction from the Web - Algorithms and Tools
Information Extraction from the Web - Algorithms and ToolsInformation Extraction from the Web - Algorithms and Tools
Information Extraction from the Web - Algorithms and Tools
Benjamin Habegger
 
Data Selection For Support Vector Machine Classifier
Data Selection For Support Vector Machine ClassifierData Selection For Support Vector Machine Classifier
Data Selection For Support Vector Machine Classifier
GUANBO
 

En vedette (8)

Data Selection For Support Vector Machine Classifier
Data Selection For Support Vector Machine ClassifierData Selection For Support Vector Machine Classifier
Data Selection For Support Vector Machine Classifier
 
Basic Mapping
Basic MappingBasic Mapping
Basic Mapping
 
Web Information Extraction Learning based on Probabilistic Graphical Models
Web Information Extraction Learning based on Probabilistic Graphical ModelsWeb Information Extraction Learning based on Probabilistic Graphical Models
Web Information Extraction Learning based on Probabilistic Graphical Models
 
Information Extraction from the Web - Algorithms and Tools
Information Extraction from the Web - Algorithms and ToolsInformation Extraction from the Web - Algorithms and Tools
Information Extraction from the Web - Algorithms and Tools
 
Data Selection For Support Vector Machine Classifier
Data Selection For Support Vector Machine ClassifierData Selection For Support Vector Machine Classifier
Data Selection For Support Vector Machine Classifier
 
Information Extraction
Information ExtractionInformation Extraction
Information Extraction
 
Information Extraction from Web-Scale N-Gram Data
Information Extraction from Web-Scale N-Gram DataInformation Extraction from Web-Scale N-Gram Data
Information Extraction from Web-Scale N-Gram Data
 
InsurTech: PwC Top Issues
InsurTech: PwC Top IssuesInsurTech: PwC Top Issues
InsurTech: PwC Top Issues
 

Similaire à Progress Report090806

ibm_research_aug_8_03
ibm_research_aug_8_03ibm_research_aug_8_03
ibm_research_aug_8_03
Attila Barta
 
DITA and Translation Best Praticices
DITA and Translation Best PraticicesDITA and Translation Best Praticices
DITA and Translation Best Praticices
Andrzej Zydroń MBCS
 
It seminar-xml serialization
It seminar-xml serializationIt seminar-xml serialization
It seminar-xml serialization
Priyojit Mondal
 
It seminar-xml serialization
It seminar-xml serializationIt seminar-xml serialization
It seminar-xml serialization
Priyojit Mondal
 
06 xml processing-in-.net
06 xml processing-in-.net06 xml processing-in-.net
06 xml processing-in-.net
glubox
 
Creating xml publisher documents with people code
Creating xml publisher documents with people codeCreating xml publisher documents with people code
Creating xml publisher documents with people code
Randall Groncki
 

Similaire à Progress Report090806 (20)

Python xml processing
Python   xml processingPython   xml processing
Python xml processing
 
File handling
File handlingFile handling
File handling
 
Unit V.pptx
Unit V.pptxUnit V.pptx
Unit V.pptx
 
File handling
File handlingFile handling
File handling
 
ibm_research_aug_8_03
ibm_research_aug_8_03ibm_research_aug_8_03
ibm_research_aug_8_03
 
DITA and Translation Best Praticices
DITA and Translation Best PraticicesDITA and Translation Best Praticices
DITA and Translation Best Praticices
 
Compare And Merge Scripts
Compare And Merge ScriptsCompare And Merge Scripts
Compare And Merge Scripts
 
It seminar-xml serialization
It seminar-xml serializationIt seminar-xml serialization
It seminar-xml serialization
 
It seminar-xml serialization
It seminar-xml serializationIt seminar-xml serialization
It seminar-xml serialization
 
06 xml processing-in-.net
06 xml processing-in-.net06 xml processing-in-.net
06 xml processing-in-.net
 
03-01-File Handling python.pptx
03-01-File Handling python.pptx03-01-File Handling python.pptx
03-01-File Handling python.pptx
 
Validating XML and JSON Documents Using Oxygen Scripting
 Validating XML and JSON Documents Using Oxygen Scripting Validating XML and JSON Documents Using Oxygen Scripting
Validating XML and JSON Documents Using Oxygen Scripting
 
Creating xml publisher documents with people code
Creating xml publisher documents with people codeCreating xml publisher documents with people code
Creating xml publisher documents with people code
 
Xml processing-by-asfak
Xml processing-by-asfakXml processing-by-asfak
Xml processing-by-asfak
 
CHAPTER 2 - FILE HANDLING-txtfile.pdf is here
CHAPTER 2 - FILE HANDLING-txtfile.pdf is hereCHAPTER 2 - FILE HANDLING-txtfile.pdf is here
CHAPTER 2 - FILE HANDLING-txtfile.pdf is here
 
Data file handling
Data file handlingData file handling
Data file handling
 
Xml and xml processor
Xml and xml processorXml and xml processor
Xml and xml processor
 
Xml and xml processor
Xml and xml processorXml and xml processor
Xml and xml processor
 
Advanced Web Programming Chapter 12
Advanced Web Programming Chapter 12Advanced Web Programming Chapter 12
Advanced Web Programming Chapter 12
 
XML
XMLXML
XML
 

Dernier

Easier, Faster, and More Powerful – Alles Neu macht der Mai -Wir durchleuchte...
Easier, Faster, and More Powerful – Alles Neu macht der Mai -Wir durchleuchte...Easier, Faster, and More Powerful – Alles Neu macht der Mai -Wir durchleuchte...
Easier, Faster, and More Powerful – Alles Neu macht der Mai -Wir durchleuchte...
panagenda
 
TrustArc Webinar - Unified Trust Center for Privacy, Security, Compliance, an...
TrustArc Webinar - Unified Trust Center for Privacy, Security, Compliance, an...TrustArc Webinar - Unified Trust Center for Privacy, Security, Compliance, an...
TrustArc Webinar - Unified Trust Center for Privacy, Security, Compliance, an...
TrustArc
 

Dernier (20)

Working together SRE & Platform Engineering
Working together SRE & Platform EngineeringWorking together SRE & Platform Engineering
Working together SRE & Platform Engineering
 
Event-Driven Architecture Masterclass: Integrating Distributed Data Stores Ac...
Event-Driven Architecture Masterclass: Integrating Distributed Data Stores Ac...Event-Driven Architecture Masterclass: Integrating Distributed Data Stores Ac...
Event-Driven Architecture Masterclass: Integrating Distributed Data Stores Ac...
 
Event-Driven Architecture Masterclass: Engineering a Robust, High-performance...
Event-Driven Architecture Masterclass: Engineering a Robust, High-performance...Event-Driven Architecture Masterclass: Engineering a Robust, High-performance...
Event-Driven Architecture Masterclass: Engineering a Robust, High-performance...
 
Vector Search @ sw2con for slideshare.pptx
Vector Search @ sw2con for slideshare.pptxVector Search @ sw2con for slideshare.pptx
Vector Search @ sw2con for slideshare.pptx
 
Generative AI Use Cases and Applications.pdf
Generative AI Use Cases and Applications.pdfGenerative AI Use Cases and Applications.pdf
Generative AI Use Cases and Applications.pdf
 
Extensible Python: Robustness through Addition - PyCon 2024
Extensible Python: Robustness through Addition - PyCon 2024Extensible Python: Robustness through Addition - PyCon 2024
Extensible Python: Robustness through Addition - PyCon 2024
 
Easier, Faster, and More Powerful – Alles Neu macht der Mai -Wir durchleuchte...
Easier, Faster, and More Powerful – Alles Neu macht der Mai -Wir durchleuchte...Easier, Faster, and More Powerful – Alles Neu macht der Mai -Wir durchleuchte...
Easier, Faster, and More Powerful – Alles Neu macht der Mai -Wir durchleuchte...
 
Intro to Passkeys and the State of Passwordless.pptx
Intro to Passkeys and the State of Passwordless.pptxIntro to Passkeys and the State of Passwordless.pptx
Intro to Passkeys and the State of Passwordless.pptx
 
Event-Driven Architecture Masterclass: Challenges in Stream Processing
Event-Driven Architecture Masterclass: Challenges in Stream ProcessingEvent-Driven Architecture Masterclass: Challenges in Stream Processing
Event-Driven Architecture Masterclass: Challenges in Stream Processing
 
How we scaled to 80K users by doing nothing!.pdf
How we scaled to 80K users by doing nothing!.pdfHow we scaled to 80K users by doing nothing!.pdf
How we scaled to 80K users by doing nothing!.pdf
 
AI mind or machine power point presentation
AI mind or machine power point presentationAI mind or machine power point presentation
AI mind or machine power point presentation
 
WebAssembly is Key to Better LLM Performance
WebAssembly is Key to Better LLM PerformanceWebAssembly is Key to Better LLM Performance
WebAssembly is Key to Better LLM Performance
 
Frisco Automating Purchase Orders with MuleSoft IDP- May 10th, 2024.pptx.pdf
Frisco Automating Purchase Orders with MuleSoft IDP- May 10th, 2024.pptx.pdfFrisco Automating Purchase Orders with MuleSoft IDP- May 10th, 2024.pptx.pdf
Frisco Automating Purchase Orders with MuleSoft IDP- May 10th, 2024.pptx.pdf
 
TrustArc Webinar - Unified Trust Center for Privacy, Security, Compliance, an...
TrustArc Webinar - Unified Trust Center for Privacy, Security, Compliance, an...TrustArc Webinar - Unified Trust Center for Privacy, Security, Compliance, an...
TrustArc Webinar - Unified Trust Center for Privacy, Security, Compliance, an...
 
Design and Development of a Provenance Capture Platform for Data Science
Design and Development of a Provenance Capture Platform for Data ScienceDesign and Development of a Provenance Capture Platform for Data Science
Design and Development of a Provenance Capture Platform for Data Science
 
How to Check CNIC Information Online with Pakdata cf
How to Check CNIC Information Online with Pakdata cfHow to Check CNIC Information Online with Pakdata cf
How to Check CNIC Information Online with Pakdata cf
 
UiPath manufacturing technology benefits and AI overview
UiPath manufacturing technology benefits and AI overviewUiPath manufacturing technology benefits and AI overview
UiPath manufacturing technology benefits and AI overview
 
The Metaverse: Are We There Yet?
The  Metaverse:    Are   We  There  Yet?The  Metaverse:    Are   We  There  Yet?
The Metaverse: Are We There Yet?
 
TEST BANK For, Information Technology Project Management 9th Edition Kathy Sc...
TEST BANK For, Information Technology Project Management 9th Edition Kathy Sc...TEST BANK For, Information Technology Project Management 9th Edition Kathy Sc...
TEST BANK For, Information Technology Project Management 9th Edition Kathy Sc...
 
State of the Smart Building Startup Landscape 2024!
State of the Smart Building Startup Landscape 2024!State of the Smart Building Startup Landscape 2024!
State of the Smart Building Startup Landscape 2024!
 

Progress Report090806

  • 1. Experiment Result for Pair Method 2009/08/06Chi-I Kuan 1
  • 2. Examinationflow 2 Combine Files as Training Data XMLFeature Analyzer Training Xml File1 Representation XMLFeature Analyzer Training Xml File2 Representation libSVM Classifier Testing Xml File1 XMLFeature Analyzer Combine Filesas Training Data Representation Model XMLFeature Analyzer Testing Xml File2 Representation Decide Mapping
  • 3. Training and Testing Data Combine two analyzed xml files as input. We has 6 bookstores’ xml file , each time we choose 2 xml file as testing data and use the remaining 4 xml files as training data. 3
  • 4.
  • 5. Combine Combine two xml analyzed files as a pair. 6
  • 6. Classifier Use libSVM to predict whether they are matching or not. 7
  • 7. Decide Mapping Check classifier’s prediction result , if the vector is prediction “Match” ,then output the vector’s attribute number as result. 8 output