Weka on Android: load precomputed model and predict new samples

Weka on Android

Imagine you want to do a machine learning prediction on Android. Usually, neither the storage capabilities (for storing the training data) nor the computational power for training such models is available on mobile devices - assuming that you use a medium or large dataset and thorough evaluation of different model types and model parametrizations with a subsequent model selection. This post demonstrates how to load and use a model file on Android, which was previously trained offline using the Weka framework.

SVM classification example with performance measures using R caret

This example is a followup of hyperparameter tuning using the e1071 package in R. This time we're using the SVM implementation from the R caret package, a binary class classification problem and some extended features that come in handy for many classification problems. For an easy start with caret take a look at one of … Continue reading SVM classification example with performance measures using R caret

mctune: multicore hyperparameter tuning in R on the example of SVM car detection

mctune In Machine Learning (ML) tasks finding good hyperparameters for machine learning models is critical (hyperparameter optimization). In R there exist some packages containing routines doing that for you using grid search (constructing and testing all possible parameters as a grid, e.g. in David Meyer's e1071 package). Besides the very good routines already contained in … Continue reading mctune: multicore hyperparameter tuning in R on the example of SVM car detection

Image classification using SVMs in R

Recently I did some Support Vector Machine (SVM) tests in R (statistical language¬†with functional parts for rapid prototyping and data analysis -- somehow similar to Matlab, but open source ;)) for my current face recognition projects. To get my SVMs up and running in R, using image data as in- and output, I wrote a … Continue reading Image classification using SVMs in R