^TagOccam's razor

Human brain digital illustration. Electrical activity, flashes and lightning on a blue background.

^{Type
post

Author
Eric Holloway
Date
October 4, 2021

Categorized
Natural Intelligence, Philosophy of Mind

Tagged
Intractable Cognition Thesis, NP-Complete domain, Occam's razor, polynomial time algorithm, Tractable Cognition Thesis}

An Alternative to the Tractable Cognition Thesis

_{The Tractable Cognition Thesis presents us with a gap in the logic when it comes to NP-Complete problems. How can we solve for it?} _{Eric Holloway

October 4, 2021

4

Natural Intelligence, Philosophy of Mind}

The Tractable Cognition Thesis is the proposal that all processes in the brain can be modeled by a polynomial time algorithm. This includes situations where the brain solves problems that are within NP-Complete domains. In the latter situation, it is assumed the brain is only solving a subset of the NP-Complete domain where the problems can be solved with a polynomial time algorithm. With these assumptions in place, the overall implication is that there is a specific polynomial time algorithm that can emulate every process in the brain. However, there is a gap in the logic when it comes to NP-Complete problems. It is well known that humans solve many problems that are in the general case NP-Complete. Route planning, Read More ›

^{Type
post

Author
Eric Holloway
Date
November 18, 2019

Categorized
Machine Learning

Tagged
Occam's razor, Probability}

Machine Learning: Using Occam’s Razor to Generalize

_{A simple thought experiment shows us how} _{Eric Holloway

November 18, 2019

4

Machine Learning}

This approach is contrary to Fisher's method where we formulate all theories before encountering the data. We came up with the model after we saw the cards we drew.

Occam’s Razor Can Shave Away Data Snooping

_{The greater an object's information content, the lower its probability.} _{Eric Holloway

November 11, 2019

4

Machine Learning, Programming}

One technique to avoid data snooping is based on the intersection of information theory and probability: An object’s probability is related to its information content. The greater an object’s information content, the lower its probability. We measure a model’s information content as the logarithmic difference between the probability that the data occurred by chance and the number of bits required to store the model. The negative exponential of the difference is the model’s probability of occurring by chance. If the data cannot be compressed, then these two values are equal. Then the model has zero information and we cannot know if the data was generated by chance or not. For a dataset that is incompressible and uninformative, swirl some tea Read More ›

^{Type
post

Author
Jonathan Bartlett
Date
June 12, 2019

Categorized
Machine Learning

Tagged
Occam's razor, Solomonoff induction}

Successful Generalization Is a Key to Learning

_{In machine learning, the Solomonoff induction helps us decide how successful a generalization is} _{Jonathan Bartlett

June 12, 2019

6

Machine Learning}

In the model of generalization set out in the paper, imperfect models can get better scores but they are discounted according to the amount of error they have.