SlideShare a Scribd company logo
CH 17
GOING A STEP BEYOND USING
SUPPORT VECTOR
MACHINES
10766012陳遠任 Jason
Revisiting the Separation
Problem
• Nonseparability of classes
• There is no straight line that traces a precise border
between different examples.
• Other options
• K-Nearest Neighbors : Ch14
• Logistic regression : Ch15
• Transforming the features : Solves the problem by
employing both feature creation
• Decision trees、Neural networks
Characteristics of
Support Vector Machines
• Binary and multiclass classification, regression, and
detection of anomalous or novelty data
• Robust handling of overfitting, noisy data, and outliers
• A capability to handle situations with many variables
• Easy and timely handling of up to about 10,000
training examples
• Automatic detection of nonlinearity in data
Explaining the Algorithm
SVM - Linear
Negative hyperplane
Positive hyperplane
Support Vector
Applying Nonlinearity
• Nonlinearly
separable points
requiring feature
transformation
(left) to be fit by a
line (right).
• Make the existing
features onto a
feature space of
higher
dimensionality
Applying Nonlinearity
• Problems and limits :
• The number of features increases exponentially, making
computations cumbersome 計算繁複
• The expansion creates many redundant features,
causing overfitting. 創造冗餘特徵
• Difficult to determine becoming linearly or not, requiring
many iterations of expansion and test
Kernel functions
• kernel functions project the original features into
a higher dimensional space by combining them
in a nonlinear way
• rely on algebra calculations
Discovering the different
kernels
• Linear: Suitable for linear
• No extra parameters
• Radial Basis Function: Suitable for non-linear
• parameters: gamma
• Polynomial: suitable for non-linear
• parameters: gamma, degree, and coef0
• Sigmoid: Binary classification like Logistic Regression
• parameters: gamma and coef0
• Custom-made kernels: Depends upon the kernel
Radial Basis Function
• An RBF kernel
that uses
diverse hyper-
parameters to
create unique
SVM solutions.
• The RBF kernel can adapt itself to different
learning strategies
• the error cost is high -> bended hyperplane
• the error cost is low -> smoother curve line
Kernels
• The polynomial and sigmoid kernels aren’t as
adaptable as RBF, thus showing more bias
• Most data problems are easily solved using the
RBF
sigmoid polynomial
Classifying and Estimating
with SVM
• handwritten recognition task
• the digits dataset (from Scikit-learn)
• nonlinear kernel, using the RBF
• a series of 8-x-8 grayscale pixel images of
handwritten numbers ranging from 0 to 9.

More Related Content

Similar to Ml ch17

The Case for Learned Index Structures
The Case for Learned Index StructuresThe Case for Learned Index Structures
The Case for Learned Index Structures
宇 傅
 
Large-Scale Lasso and Elastic-Net Regularized Generalized Linear Models (DB T...
Large-Scale Lasso and Elastic-Net Regularized Generalized Linear Models (DB T...Large-Scale Lasso and Elastic-Net Regularized Generalized Linear Models (DB T...
Large-Scale Lasso and Elastic-Net Regularized Generalized Linear Models (DB T...
Spark Summit
 
Week5-Faster R-CNN.pptx
Week5-Faster R-CNN.pptxWeek5-Faster R-CNN.pptx
Week5-Faster R-CNN.pptx
fahmi324663
 
How Machine Learning Helps Organizations to Work More Efficiently?
How Machine Learning Helps Organizations to Work More Efficiently?How Machine Learning Helps Organizations to Work More Efficiently?
How Machine Learning Helps Organizations to Work More Efficiently?
Tuan Yang
 
201907 AutoML and Neural Architecture Search
201907 AutoML and Neural Architecture Search201907 AutoML and Neural Architecture Search
201907 AutoML and Neural Architecture Search
DaeJin Kim
 
Faster R-CNN - PR012
Faster R-CNN - PR012Faster R-CNN - PR012
Faster R-CNN - PR012
Jinwon Lee
 
[Q-tangled 22] Deconstructing Quantum Machine Learning Algorithms - Sasha Laz...
[Q-tangled 22] Deconstructing Quantum Machine Learning Algorithms - Sasha Laz...[Q-tangled 22] Deconstructing Quantum Machine Learning Algorithms - Sasha Laz...
[Q-tangled 22] Deconstructing Quantum Machine Learning Algorithms - Sasha Laz...
DataScienceConferenc1
 
Bootstrap Your Own Latent: A New Approach to Self-Supervised Learning
Bootstrap Your Own Latent: A New Approach to Self-Supervised LearningBootstrap Your Own Latent: A New Approach to Self-Supervised Learning
Bootstrap Your Own Latent: A New Approach to Self-Supervised Learning
Sungchul Kim
 
Towards Detecting Performance Anti-patterns Using Classification Techniques
Towards Detecting Performance Anti-patterns Using Classification TechniquesTowards Detecting Performance Anti-patterns Using Classification Techniques
Towards Detecting Performance Anti-patterns Using Classification Techniques
James Hill
 
Welch Verolog 2013
Welch Verolog 2013Welch Verolog 2013
Welch Verolog 2013
Philip Welch
 
Introduction to deep learning
Introduction to deep learningIntroduction to deep learning
Introduction to deep learning
Vishwas Lele
 
Machine Learning Workshop
Machine Learning WorkshopMachine Learning Workshop
Machine Learning Workshop
Osman Ramadan
 
Improving region based CNN object detector using bayesian optimization
Improving region based CNN object detector using bayesian optimizationImproving region based CNN object detector using bayesian optimization
Improving region based CNN object detector using bayesian optimization
Amgad Muhammad
 
Performance Benchmarking of the R Programming Environment on the Stampede 1.5...
Performance Benchmarking of the R Programming Environment on the Stampede 1.5...Performance Benchmarking of the R Programming Environment on the Stampede 1.5...
Performance Benchmarking of the R Programming Environment on the Stampede 1.5...
James McCombs
 
AMBER presentation
AMBER presentationAMBER presentation
AMBER presentation
Giorgio Orsi
 
Performance van Java 8 en verder - Jeroen Borgers
Performance van Java 8 en verder - Jeroen BorgersPerformance van Java 8 en verder - Jeroen Borgers
Performance van Java 8 en verder - Jeroen Borgers
NLJUG
 
An Introduction to Deep Learning
An Introduction to Deep LearningAn Introduction to Deep Learning
An Introduction to Deep Learning
milad abbasi
 
Introduction to Deep Learning
Introduction to Deep LearningIntroduction to Deep Learning
Introduction to Deep Learning
Mehrnaz Faraz
 
Support vector machine learning.pptx
Support vector machine learning.pptxSupport vector machine learning.pptx
Support vector machine learning.pptx
Abhiroop Bhattacharya
 
DQN (Deep Q-Network)
DQN (Deep Q-Network)DQN (Deep Q-Network)
DQN (Deep Q-Network)
Dong Guo
 

Similar to Ml ch17 (20)

The Case for Learned Index Structures
The Case for Learned Index StructuresThe Case for Learned Index Structures
The Case for Learned Index Structures
 
Large-Scale Lasso and Elastic-Net Regularized Generalized Linear Models (DB T...
Large-Scale Lasso and Elastic-Net Regularized Generalized Linear Models (DB T...Large-Scale Lasso and Elastic-Net Regularized Generalized Linear Models (DB T...
Large-Scale Lasso and Elastic-Net Regularized Generalized Linear Models (DB T...
 
Week5-Faster R-CNN.pptx
Week5-Faster R-CNN.pptxWeek5-Faster R-CNN.pptx
Week5-Faster R-CNN.pptx
 
How Machine Learning Helps Organizations to Work More Efficiently?
How Machine Learning Helps Organizations to Work More Efficiently?How Machine Learning Helps Organizations to Work More Efficiently?
How Machine Learning Helps Organizations to Work More Efficiently?
 
201907 AutoML and Neural Architecture Search
201907 AutoML and Neural Architecture Search201907 AutoML and Neural Architecture Search
201907 AutoML and Neural Architecture Search
 
Faster R-CNN - PR012
Faster R-CNN - PR012Faster R-CNN - PR012
Faster R-CNN - PR012
 
[Q-tangled 22] Deconstructing Quantum Machine Learning Algorithms - Sasha Laz...
[Q-tangled 22] Deconstructing Quantum Machine Learning Algorithms - Sasha Laz...[Q-tangled 22] Deconstructing Quantum Machine Learning Algorithms - Sasha Laz...
[Q-tangled 22] Deconstructing Quantum Machine Learning Algorithms - Sasha Laz...
 
Bootstrap Your Own Latent: A New Approach to Self-Supervised Learning
Bootstrap Your Own Latent: A New Approach to Self-Supervised LearningBootstrap Your Own Latent: A New Approach to Self-Supervised Learning
Bootstrap Your Own Latent: A New Approach to Self-Supervised Learning
 
Towards Detecting Performance Anti-patterns Using Classification Techniques
Towards Detecting Performance Anti-patterns Using Classification TechniquesTowards Detecting Performance Anti-patterns Using Classification Techniques
Towards Detecting Performance Anti-patterns Using Classification Techniques
 
Welch Verolog 2013
Welch Verolog 2013Welch Verolog 2013
Welch Verolog 2013
 
Introduction to deep learning
Introduction to deep learningIntroduction to deep learning
Introduction to deep learning
 
Machine Learning Workshop
Machine Learning WorkshopMachine Learning Workshop
Machine Learning Workshop
 
Improving region based CNN object detector using bayesian optimization
Improving region based CNN object detector using bayesian optimizationImproving region based CNN object detector using bayesian optimization
Improving region based CNN object detector using bayesian optimization
 
Performance Benchmarking of the R Programming Environment on the Stampede 1.5...
Performance Benchmarking of the R Programming Environment on the Stampede 1.5...Performance Benchmarking of the R Programming Environment on the Stampede 1.5...
Performance Benchmarking of the R Programming Environment on the Stampede 1.5...
 
AMBER presentation
AMBER presentationAMBER presentation
AMBER presentation
 
Performance van Java 8 en verder - Jeroen Borgers
Performance van Java 8 en verder - Jeroen BorgersPerformance van Java 8 en verder - Jeroen Borgers
Performance van Java 8 en verder - Jeroen Borgers
 
An Introduction to Deep Learning
An Introduction to Deep LearningAn Introduction to Deep Learning
An Introduction to Deep Learning
 
Introduction to Deep Learning
Introduction to Deep LearningIntroduction to Deep Learning
Introduction to Deep Learning
 
Support vector machine learning.pptx
Support vector machine learning.pptxSupport vector machine learning.pptx
Support vector machine learning.pptx
 
DQN (Deep Q-Network)
DQN (Deep Q-Network)DQN (Deep Q-Network)
DQN (Deep Q-Network)
 

More from Jason Chen

Optimize0530v 3
Optimize0530v 3Optimize0530v 3
Optimize0530v 3
Jason Chen
 
Ai robot
Ai robotAi robot
Ai robot
Jason Chen
 
Outlier detection in audit logs for application systems
Outlier detection in audit logs for application systemsOutlier detection in audit logs for application systems
Outlier detection in audit logs for application systems
Jason Chen
 
PHILIPPINE JASON 0530
 PHILIPPINE JASON 0530 PHILIPPINE JASON 0530
PHILIPPINE JASON 0530
Jason Chen
 
JASON - Data mining
JASON - Data miningJASON - Data mining
JASON - Data mining
Jason Chen
 
10766012 ranalitics
10766012 ranalitics10766012 ranalitics
10766012 ranalitics
Jason Chen
 
Post Big Data 0530
Post Big Data 0530Post Big Data 0530
Post Big Data 0530
Jason Chen
 

More from Jason Chen (7)

Optimize0530v 3
Optimize0530v 3Optimize0530v 3
Optimize0530v 3
 
Ai robot
Ai robotAi robot
Ai robot
 
Outlier detection in audit logs for application systems
Outlier detection in audit logs for application systemsOutlier detection in audit logs for application systems
Outlier detection in audit logs for application systems
 
PHILIPPINE JASON 0530
 PHILIPPINE JASON 0530 PHILIPPINE JASON 0530
PHILIPPINE JASON 0530
 
JASON - Data mining
JASON - Data miningJASON - Data mining
JASON - Data mining
 
10766012 ranalitics
10766012 ranalitics10766012 ranalitics
10766012 ranalitics
 
Post Big Data 0530
Post Big Data 0530Post Big Data 0530
Post Big Data 0530
 

Recently uploaded

Everything you wanted to know about LIHTC
Everything you wanted to know about LIHTCEverything you wanted to know about LIHTC
Everything you wanted to know about LIHTC
Roger Valdez
 
Experts live - Improving user adoption with AI
Experts live - Improving user adoption with AIExperts live - Improving user adoption with AI
Experts live - Improving user adoption with AI
jitskeb
 
一比一原版(Glasgow毕业证书)格拉斯哥大学毕业证如何办理
一比一原版(Glasgow毕业证书)格拉斯哥大学毕业证如何办理一比一原版(Glasgow毕业证书)格拉斯哥大学毕业证如何办理
一比一原版(Glasgow毕业证书)格拉斯哥大学毕业证如何办理
g4dpvqap0
 
My burning issue is homelessness K.C.M.O.
My burning issue is homelessness K.C.M.O.My burning issue is homelessness K.C.M.O.
My burning issue is homelessness K.C.M.O.
rwarrenll
 
ViewShift: Hassle-free Dynamic Policy Enforcement for Every Data Lake
ViewShift: Hassle-free Dynamic Policy Enforcement for Every Data LakeViewShift: Hassle-free Dynamic Policy Enforcement for Every Data Lake
ViewShift: Hassle-free Dynamic Policy Enforcement for Every Data Lake
Walaa Eldin Moustafa
 
Predictably Improve Your B2B Tech Company's Performance by Leveraging Data
Predictably Improve Your B2B Tech Company's Performance by Leveraging DataPredictably Improve Your B2B Tech Company's Performance by Leveraging Data
Predictably Improve Your B2B Tech Company's Performance by Leveraging Data
Kiwi Creative
 
一比一原版(UCSF文凭证书)旧金山分校毕业证如何办理
一比一原版(UCSF文凭证书)旧金山分校毕业证如何办理一比一原版(UCSF文凭证书)旧金山分校毕业证如何办理
一比一原版(UCSF文凭证书)旧金山分校毕业证如何办理
nuttdpt
 
Population Growth in Bataan: The effects of population growth around rural pl...
Population Growth in Bataan: The effects of population growth around rural pl...Population Growth in Bataan: The effects of population growth around rural pl...
Population Growth in Bataan: The effects of population growth around rural pl...
Bill641377
 
A presentation that explain the Power BI Licensing
A presentation that explain the Power BI LicensingA presentation that explain the Power BI Licensing
A presentation that explain the Power BI Licensing
AlessioFois2
 
End-to-end pipeline agility - Berlin Buzzwords 2024
End-to-end pipeline agility - Berlin Buzzwords 2024End-to-end pipeline agility - Berlin Buzzwords 2024
End-to-end pipeline agility - Berlin Buzzwords 2024
Lars Albertsson
 
Palo Alto Cortex XDR presentation .......
Palo Alto Cortex XDR presentation .......Palo Alto Cortex XDR presentation .......
Palo Alto Cortex XDR presentation .......
Sachin Paul
 
一比一原版(UCSB文凭证书)圣芭芭拉分校毕业证如何办理
一比一原版(UCSB文凭证书)圣芭芭拉分校毕业证如何办理一比一原版(UCSB文凭证书)圣芭芭拉分校毕业证如何办理
一比一原版(UCSB文凭证书)圣芭芭拉分校毕业证如何办理
nuttdpt
 
一比一原版(牛布毕业证书)牛津布鲁克斯大学毕业证如何办理
一比一原版(牛布毕业证书)牛津布鲁克斯大学毕业证如何办理一比一原版(牛布毕业证书)牛津布鲁克斯大学毕业证如何办理
一比一原版(牛布毕业证书)牛津布鲁克斯大学毕业证如何办理
74nqk8xf
 
一比一原版(GWU,GW文凭证书)乔治·华盛顿大学毕业证如何办理
一比一原版(GWU,GW文凭证书)乔治·华盛顿大学毕业证如何办理一比一原版(GWU,GW文凭证书)乔治·华盛顿大学毕业证如何办理
一比一原版(GWU,GW文凭证书)乔治·华盛顿大学毕业证如何办理
bopyb
 
一比一原版(爱大毕业证书)爱丁堡大学毕业证如何办理
一比一原版(爱大毕业证书)爱丁堡大学毕业证如何办理一比一原版(爱大毕业证书)爱丁堡大学毕业证如何办理
一比一原版(爱大毕业证书)爱丁堡大学毕业证如何办理
g4dpvqap0
 
在线办理(英国UCA毕业证书)创意艺术大学毕业证在读证明一模一样
在线办理(英国UCA毕业证书)创意艺术大学毕业证在读证明一模一样在线办理(英国UCA毕业证书)创意艺术大学毕业证在读证明一模一样
在线办理(英国UCA毕业证书)创意艺术大学毕业证在读证明一模一样
v7oacc3l
 
Challenges of Nation Building-1.pptx with more important
Challenges of Nation Building-1.pptx with more importantChallenges of Nation Building-1.pptx with more important
Challenges of Nation Building-1.pptx with more important
Sm321
 
University of New South Wales degree offer diploma Transcript
University of New South Wales degree offer diploma TranscriptUniversity of New South Wales degree offer diploma Transcript
University of New South Wales degree offer diploma Transcript
soxrziqu
 
DSSML24_tspann_CodelessGenerativeAIPipelines
DSSML24_tspann_CodelessGenerativeAIPipelinesDSSML24_tspann_CodelessGenerativeAIPipelines
DSSML24_tspann_CodelessGenerativeAIPipelines
Timothy Spann
 
一比一原版(UO毕业证)渥太华大学毕业证如何办理
一比一原版(UO毕业证)渥太华大学毕业证如何办理一比一原版(UO毕业证)渥太华大学毕业证如何办理
一比一原版(UO毕业证)渥太华大学毕业证如何办理
aqzctr7x
 

Recently uploaded (20)

Everything you wanted to know about LIHTC
Everything you wanted to know about LIHTCEverything you wanted to know about LIHTC
Everything you wanted to know about LIHTC
 
Experts live - Improving user adoption with AI
Experts live - Improving user adoption with AIExperts live - Improving user adoption with AI
Experts live - Improving user adoption with AI
 
一比一原版(Glasgow毕业证书)格拉斯哥大学毕业证如何办理
一比一原版(Glasgow毕业证书)格拉斯哥大学毕业证如何办理一比一原版(Glasgow毕业证书)格拉斯哥大学毕业证如何办理
一比一原版(Glasgow毕业证书)格拉斯哥大学毕业证如何办理
 
My burning issue is homelessness K.C.M.O.
My burning issue is homelessness K.C.M.O.My burning issue is homelessness K.C.M.O.
My burning issue is homelessness K.C.M.O.
 
ViewShift: Hassle-free Dynamic Policy Enforcement for Every Data Lake
ViewShift: Hassle-free Dynamic Policy Enforcement for Every Data LakeViewShift: Hassle-free Dynamic Policy Enforcement for Every Data Lake
ViewShift: Hassle-free Dynamic Policy Enforcement for Every Data Lake
 
Predictably Improve Your B2B Tech Company's Performance by Leveraging Data
Predictably Improve Your B2B Tech Company's Performance by Leveraging DataPredictably Improve Your B2B Tech Company's Performance by Leveraging Data
Predictably Improve Your B2B Tech Company's Performance by Leveraging Data
 
一比一原版(UCSF文凭证书)旧金山分校毕业证如何办理
一比一原版(UCSF文凭证书)旧金山分校毕业证如何办理一比一原版(UCSF文凭证书)旧金山分校毕业证如何办理
一比一原版(UCSF文凭证书)旧金山分校毕业证如何办理
 
Population Growth in Bataan: The effects of population growth around rural pl...
Population Growth in Bataan: The effects of population growth around rural pl...Population Growth in Bataan: The effects of population growth around rural pl...
Population Growth in Bataan: The effects of population growth around rural pl...
 
A presentation that explain the Power BI Licensing
A presentation that explain the Power BI LicensingA presentation that explain the Power BI Licensing
A presentation that explain the Power BI Licensing
 
End-to-end pipeline agility - Berlin Buzzwords 2024
End-to-end pipeline agility - Berlin Buzzwords 2024End-to-end pipeline agility - Berlin Buzzwords 2024
End-to-end pipeline agility - Berlin Buzzwords 2024
 
Palo Alto Cortex XDR presentation .......
Palo Alto Cortex XDR presentation .......Palo Alto Cortex XDR presentation .......
Palo Alto Cortex XDR presentation .......
 
一比一原版(UCSB文凭证书)圣芭芭拉分校毕业证如何办理
一比一原版(UCSB文凭证书)圣芭芭拉分校毕业证如何办理一比一原版(UCSB文凭证书)圣芭芭拉分校毕业证如何办理
一比一原版(UCSB文凭证书)圣芭芭拉分校毕业证如何办理
 
一比一原版(牛布毕业证书)牛津布鲁克斯大学毕业证如何办理
一比一原版(牛布毕业证书)牛津布鲁克斯大学毕业证如何办理一比一原版(牛布毕业证书)牛津布鲁克斯大学毕业证如何办理
一比一原版(牛布毕业证书)牛津布鲁克斯大学毕业证如何办理
 
一比一原版(GWU,GW文凭证书)乔治·华盛顿大学毕业证如何办理
一比一原版(GWU,GW文凭证书)乔治·华盛顿大学毕业证如何办理一比一原版(GWU,GW文凭证书)乔治·华盛顿大学毕业证如何办理
一比一原版(GWU,GW文凭证书)乔治·华盛顿大学毕业证如何办理
 
一比一原版(爱大毕业证书)爱丁堡大学毕业证如何办理
一比一原版(爱大毕业证书)爱丁堡大学毕业证如何办理一比一原版(爱大毕业证书)爱丁堡大学毕业证如何办理
一比一原版(爱大毕业证书)爱丁堡大学毕业证如何办理
 
在线办理(英国UCA毕业证书)创意艺术大学毕业证在读证明一模一样
在线办理(英国UCA毕业证书)创意艺术大学毕业证在读证明一模一样在线办理(英国UCA毕业证书)创意艺术大学毕业证在读证明一模一样
在线办理(英国UCA毕业证书)创意艺术大学毕业证在读证明一模一样
 
Challenges of Nation Building-1.pptx with more important
Challenges of Nation Building-1.pptx with more importantChallenges of Nation Building-1.pptx with more important
Challenges of Nation Building-1.pptx with more important
 
University of New South Wales degree offer diploma Transcript
University of New South Wales degree offer diploma TranscriptUniversity of New South Wales degree offer diploma Transcript
University of New South Wales degree offer diploma Transcript
 
DSSML24_tspann_CodelessGenerativeAIPipelines
DSSML24_tspann_CodelessGenerativeAIPipelinesDSSML24_tspann_CodelessGenerativeAIPipelines
DSSML24_tspann_CodelessGenerativeAIPipelines
 
一比一原版(UO毕业证)渥太华大学毕业证如何办理
一比一原版(UO毕业证)渥太华大学毕业证如何办理一比一原版(UO毕业证)渥太华大学毕业证如何办理
一比一原版(UO毕业证)渥太华大学毕业证如何办理
 

Ml ch17

  • 1. CH 17 GOING A STEP BEYOND USING SUPPORT VECTOR MACHINES 10766012陳遠任 Jason
  • 2. Revisiting the Separation Problem • Nonseparability of classes • There is no straight line that traces a precise border between different examples. • Other options • K-Nearest Neighbors : Ch14 • Logistic regression : Ch15 • Transforming the features : Solves the problem by employing both feature creation • Decision trees、Neural networks
  • 3. Characteristics of Support Vector Machines • Binary and multiclass classification, regression, and detection of anomalous or novelty data • Robust handling of overfitting, noisy data, and outliers • A capability to handle situations with many variables • Easy and timely handling of up to about 10,000 training examples • Automatic detection of nonlinearity in data
  • 5. SVM - Linear Negative hyperplane Positive hyperplane Support Vector
  • 6. Applying Nonlinearity • Nonlinearly separable points requiring feature transformation (left) to be fit by a line (right). • Make the existing features onto a feature space of higher dimensionality
  • 7. Applying Nonlinearity • Problems and limits : • The number of features increases exponentially, making computations cumbersome 計算繁複 • The expansion creates many redundant features, causing overfitting. 創造冗餘特徵 • Difficult to determine becoming linearly or not, requiring many iterations of expansion and test
  • 8. Kernel functions • kernel functions project the original features into a higher dimensional space by combining them in a nonlinear way • rely on algebra calculations
  • 9. Discovering the different kernels • Linear: Suitable for linear • No extra parameters • Radial Basis Function: Suitable for non-linear • parameters: gamma • Polynomial: suitable for non-linear • parameters: gamma, degree, and coef0 • Sigmoid: Binary classification like Logistic Regression • parameters: gamma and coef0 • Custom-made kernels: Depends upon the kernel
  • 10. Radial Basis Function • An RBF kernel that uses diverse hyper- parameters to create unique SVM solutions. • The RBF kernel can adapt itself to different learning strategies • the error cost is high -> bended hyperplane • the error cost is low -> smoother curve line
  • 11. Kernels • The polynomial and sigmoid kernels aren’t as adaptable as RBF, thus showing more bias • Most data problems are easily solved using the RBF sigmoid polynomial
  • 12. Classifying and Estimating with SVM • handwritten recognition task • the digits dataset (from Scikit-learn) • nonlinear kernel, using the RBF • a series of 8-x-8 grayscale pixel images of handwritten numbers ranging from 0 to 9.