Tag: SVM

This is the last post in series about Support Vector Machine classifier. We already feel the basics of SVM. We have our data preprocessed. Finally, we know the influence of some major hyperparameters on the classifier. Now, let's choose proper hyperparameters for a given problem. This is done by validation or cross-validation. These techniques are very common in Machine Learning and are also helpful in finding a proper SVM model. The example will cover building the classifier for the foreground/background estimation problem in Flover project.
More SVM model selection - how to adjust all these knobs pt. 2

Flover Project

This time we are going to discuss the influence of two basic variables on the quality of SVM classifier. They are called hyperparameters to distinguish them from the parameters optimized in a machine learning procedures. Two previous posts introduced Support Vector Machine itself and data preprocessing for this classifier. As in other Machine Learning techniques there is also a need to properly adjust some system variables to find the best model for our needs. Here, we will focus on description of complexity parameter and gamma parameter from the Gaussian kernel. In the next article we will find an optimum SVM model for the foreground/background estimation problem in Flover project using model validation techniques.
More SVM model selection - how to adjust all these knobs pt. 1

Flover Project

Data preprocessing is a very important and quite underestimated step in Machine Learning pipelines. It provides cleaned and relevant datasets which then can be used in further steps like classification or regression. I will describe a study case for data which is fed to the SVM classifier to predict if a given image segment belongs to foreground or background. This is a second article about Support Vector Machine which is used for image segmentation in my flower species recognition project Flover.

More Data preprocessing for SVM classifier

Flover Project

Ze względu na to, że przy operacji oddzielania tła od obrazu w projekcie Flover używam algorytmu Support Vector Machine (SVM), postaram się go dzisiaj przystępnie opisać. To bardzo popularna metoda znana ze swojej skuteczności w dziedzinie Machine Learning. Ma za zadanie jak najlepiej odseparować od siebie elementy różnego typu ze zbioru uczącego, czyli umieścić je w optymalnie oddzielonych grupach.
More Support Vector Machine

Flover Project