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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
13 Comments
Kernels
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 separates two classes of data points. This has … Continue reading
Posted in Classification, Normalization/Kernels
18 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