Category Archives: Normalization/Kernels

Gaussian kernels

In order to give a proper introduction to Gaussian kernels, this week’s post is going to start out a little bit more abstract than usual. This level of abstraction isn’t strictly necessary to understand how Gaussian kernels work, but the … Continue reading

Posted in Normalization/Kernels | 5 Comments


Over the last few weeks, I’ve introduced two classification methods – Support Vector Machines (SVM) and Logistic Regression – that attempt to find a line, plane or hyperplane (depending on the dimension) that┬áseparates two classes of data points. This has … Continue reading

Posted in Classification, Normalization/Kernels | 12 Comments

Data Normalization

In the last post, on nearest neighbors classification, we used the “distance” between different pairs of points to decide which class each new data point should be placed into. The problem is that there are different ways to calculate distance … Continue reading

Posted in Normalization/Kernels | 3 Comments