From the post:
The Perceptron (Rosenblatt, 1957) is one of the oldest and simplest Machine Learning algorithms. It’s also trivial to kernelize, which makes it an ideal candidate to gain insights on kernel methods.
The original paper by F. Rosenblatt, The Perceptron: A Probabilistic Model for Information Storage and Organization in the Brain, Psychological Review, Vol. 65, No. 6, 1958.
Good way to learn more about kernel methods.
I have included a link to the original paper by Rosenblatt.
- What do you make of Rosenblatt’s choice to not use symbolic or Boolean logic?
- What do you make of the continued efforts (think Cyc/SUMA) to use symbolic or Boolean logic?
- Is knowledge/information probabilistic?
There are no certain answers to these questions, I am interested in how you approach discussing them.