Hub
Pricing About
  • Software
  • Blog
  • Forum
  • Events
  • Documentation
  • About KNIME
  • KNIME Community Hub
  • Nodes
  • SVM Learner
NodeNode / Learner

SVM Learner

Analytics Mining SVM
Drag & drop
Like
Copy short link

This node trains a support vector machine on the input data. It supports a number of different kernels (HyperTangent, Polynomial and RBF). The SVM learner supports multiple class problems as well (by computing the hyperplane between each class and the rest), but note that this will increase the runtime.

The SVM learning algorithm used is described in the following papers: Fast Training of Support Vector Machines using Sequential Minimal Optimization , by John C. Platt and Improvements to Platt's SMO Algorithm for SVM Classifier Design , by S. S. Keerthi et. al.

Node details

Input ports
  1. Type: Table
    Training Data
    Datatable with training data
Output ports
  1. Type: PMML
    Trained SVM
    Trained Support Vector Machine

Extension

The SVM Learner node is part of this extension:

  1. Go to item

Related workflows & nodes

  1. Go to item
    SVM on iris dataset
    TheGuideBook SVM Classification
    +2
    This workflow solves a classification problem on the iris dataset using Support Vector Ma…
    knime > Academic Alliance > Guide to Intelligent Data Science > Example Workflows > Chapter9 > 03_SVM
    knime
  2. Go to item
    SVM Exercise with Parameter Optimization
    SVM Support vector machine Classification
    +4
    Exercise for SVM. Classification of 2D silhouette attributes with SVM classifier. Oprimiz…
    knime > Academic Alliance > Guide to Intelligent Data Science > Exercises > Chapter9_SVM > SVM_Solution
    knime
  3. Go to item
    SVM on iris dataset
    TheGuideBook SVM Classification
    +2
    This workflow solves a classification problem on the iris dataset using Support Vector Ma…
    jessi > Public > 03_SVM
    jessi
  4. Go to item
    SVM Exercise with Parameter Optimization
    SVM Support vector machine Classification
    Exercise for SVM. Classification of 2D silhouette attributes with SVM classifier. Oprimiz…
    hayasaka > Public > SVM
    hayasaka
  5. Go to item
    Cross Validation with SVM and Parameter Optimization
    A Cross-Validation setup is provided by using a Support-Vector-Machine (SVM) as base lear…
    knime > Examples > 04_Analytics > 11_Optimization > 07_Cross_Validation_with_SVM_and_Parameter_Optimization
    knime
  6. Go to item
    How to use normalization in a data science solution
    Machine learning Data mining TheGuideBook
    +4
    This is how to apply normalization correctly in a data science problem. The normalization…
    knime > Academic Alliance > Guide to Intelligent Data Science > Example Workflows > Chapter6 > 02_How_to_use_normalization
    knime
  7. Go to item
    Ash_5
    ashwati_2309 > Public > Ash_5
    ashwati_2309
  8. Go to item
    How to use the Prediction Fusion node
    This workflow shows how the prediction fusion node can be used to combine the predictions…
    knime > Examples > 04_Analytics > 13_Meta_Learning > 01_Combining_Classifiers_using_Prediction_Fusion
    knime
  9. Go to item
    MOE 11 - SVM Target Prediction
    guido_kirsten > Public > MOE 11 - SVM Target Prediction > MOE 11 - SVM Target Prediction
    guido_kirsten
  10. Go to item
    OptimzeSVM
    jallmer > Public > DMML > OptimzeSVM
    jallmer
  1. Go to item
  2. Go to item
  3. Go to item
  4. Go to item
  5. Go to item
  6. Go to item

KNIME
Open for Innovation

KNIME AG
Talacker 50
8001 Zurich, Switzerland
  • Software
  • Getting started
  • Documentation
  • E-Learning course
  • Solutions
  • KNIME Hub
  • KNIME Forum
  • Blog
  • Events
  • Partner
  • Developers
  • KNIME Home
  • KNIME Open Source Story
  • Careers
  • Contact us
Download KNIME Analytics Platform Read more on KNIME Business Hub
© 2023 KNIME AG. All rights reserved.
  • Trademarks
  • Imprint
  • Privacy
  • Terms & Conditions
  • Credits