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I'm kinda new to machine learning and wanted to know if we could use multiple machine learning algorithms, for example, SVM and backpropagation together to solve a particular problem.

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  • $\begingroup$ yes u can..ex: .i have done random forest for regression problem and again i applied linear regression for the output...the same way based on u r problem u can do $\endgroup$ – sai saran Nov 14 '18 at 14:48
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You can train multiple machine learning models with same data and based on accuracy and confusion_matrix response you can decide which one to use.

In theory, you don't need to combine/merge two different ML Models (you can enhance your pre-processing) but if you still want to use different models there are two approaches:

  1. Ensemble (available with three categories - a) Bagging b) Boosting c) Stacking
  2. Hybrid (This approach allow users to create own models (or use existing) and combine them for better prediction)

Note: You need to be careful with individual algorithm response before combining them together :)

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In a classification/regression task you can use back propagation and SVM:

  • Backpropagation: use a neural network as feature extractor
  • SVM: use it to perform classification/regression with the features extracted with the neural network

In deed, in neural networks back-propagation and other well-known machine learning techniques are used together. For example, when a sigmoid layer is used as the classification layer for a binary classification neural net, a logistic regression is performed and optimized through back-propagation

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