Author Archives: jamesdmccaffrey

The Learning Update Rule for Kernel Logistic Regression

Regular logistic regression (LR) can make predictions for binary data (0 or 1) based on two or more numeric variables. For example you might want to use LR to predict if a person is Male (0) or Female (1) based … Continue reading

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Generating Non Linearly Separable Test Data

This morning I was working on a kernel logistic regression (KLR) problem. Regular logistic regression (LR) is perhaps the simplest form of machine learning (ML). It’s used when the problem is to predict a binary value, using two or more … Continue reading

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Brain Damage, Idiot Savants, and Neural Networks

It’s been well-known for a long time that every now and then, someone who suffers a serious brain injury to the left hemisphere can lose much cognitive ability but gain superhuman powers in some areas. For example, a person can … Continue reading

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The 2017 Interop ITX Conference is Coming Up Soon

I get quite a few invitations to speak at conferences, but I usually say “yes” only to the events where I think I can do good for my company and also learn something useful myself. One of these events is … Continue reading

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Support Vector Machine Classification and Kernels

Even among my engineering colleagues who work with machine learning quite often, the basic ideas behind support vector machine (SVM) classification are a bit hazy. I believe this mild confusion is due, in part, to the fact that SVM classification … Continue reading

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Support Vector Machine Classification

Take a look at the graph below. Each of the nine data points belongs to one of three classes, red = 1, blue = 2, green = 3. The goal of a machine learning classifier is to create a prediction … Continue reading

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Using the CNTK Built-In File Reader Functions

Microsoft CNTK is a very powerful code library for machine learning. The library is written in C++ but has a Python API for convenience. I’ve been taking a very deep dive into CNTK v2.0 Release Candidate 1. The v2.0 should … Continue reading

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