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203.6.9 SVM : Conclusion

Advantages, disadvantages and application of SVM

SVM Advantages & Disadvantages

SVM Advantages

In previous section, we studied about  Digit Recognition using SVM

  • SVM’s are very good when we have no idea on the data.
  • Works well with even unstructured and semi structured data like text, Images and trees.
  • The kernel trick is real strength of SVM. With an appropriate kernel function, we can solve any complex problem.
  • Unlike in neural networks, SVM is not solved for local optima.
  • It scales relatively well to high dimensional data.
  • SVM models have generalization in practice, the risk of overfitting is less in SVM.

SVM Disadvantages

  • Choosing a “good” kernel function is not easy.
  • Long training time for large datasets.
  • Difficult to understand and interpret the final model, variable weights and individual impact.
  • Since the final model is not so easy to see, we can not do small calibrations to the model hence its tough to incorporate our business logic.

SVM Application

  • Protein Structure Prediction
  • Intrusion Detection
  • Handwriting Recognition
  • Detecting Steganography in digital images
  • Breast Cancer Diagnosis.


    • Many software tools are available for SVM implementation.
    • SVMs are really good for text classification.
    • SVMs are good at finding the best linear separator. The kernel trick makes SVMs non-linear learning algorithms.
    • Choosing an appropriate kernel is the key for good SVM and choosing the right kernel function is not easy.
    • We need to be patient while building SVMs on large datasets. They take a lot of time for training.


    In the next post Random Forests and Boosting  Wisdom of Crowd

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