Part 2. How We Do It
In the previous posting, I briefly introduced the Error Regression Scheme (ERS). In this posting, I’ll talk a bit more about how we actually use the ERS in our experiments.
Continue readingPart 1. What It Is and Why We Do It
In this posting, I’ll introduce an interesting method that we often use in our research, called the Error Regression Scheme (ERS) [1-2]. In short, the ERS is a sort of online optimization technique, but it is different from other techniques in several ways. For instance, during the ERS, the weights are not updated. Instead, the neuron’s actual values are updated to minimize the error at the output. The ERS is a kind of prediction error minimization mechanism and there are several (philosophical) thoughts behind it. I’ll talk about them later in other postings. Let’s begin with what it is and why we use it.
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