hunch_net hunch_net-2005 hunch_net-2005-7 knowledge-graph by maker-knowledge-mining

7 hunch net-2005-01-31-Watchword: Assumption


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Introduction: “Assumption” is another word to be careful with in machine learning because it is used in several ways. Assumption = Bias There are several ways to see that some form of ‘bias’ (= preferring of one solution over another) is necessary. This is obvious in an adversarial setting. A good bit of work has been expended explaining this in other settings with “ no free lunch ” theorems. This is a usage specialized to learning which is particularly common when talking about priors for Bayesian Learning. Assumption = “if” of a theorem The assumptions are the ‘if’ part of the ‘if-then’ in a theorem. This is a fairly common usage. Assumption = Axiom The assumptions are the things that we assume are true, but which we cannot verify. Examples are “the IID assumption” or “my problem is a DNF on a small number of bits”. This is the usage which I prefer. One difficulty with any use of the word “assumption” is that you often encounter “if assumption then conclusion so if no


Summary: the most important sentenses genereted by tfidf model

sentIndex sentText sentNum sentScore

1 “Assumption” is another word to be careful with in machine learning because it is used in several ways. [sent-1, score-0.383]

2 Assumption = Bias There are several ways to see that some form of ‘bias’ (= preferring of one solution over another) is necessary. [sent-2, score-0.321]

3 A good bit of work has been expended explaining this in other settings with “ no free lunch ” theorems. [sent-4, score-0.562]

4 This is a usage specialized to learning which is particularly common when talking about priors for Bayesian Learning. [sent-5, score-0.673]

5 Assumption = “if” of a theorem The assumptions are the ‘if’ part of the ‘if-then’ in a theorem. [sent-6, score-0.267]

6 Assumption = Axiom The assumptions are the things that we assume are true, but which we cannot verify. [sent-8, score-0.229]

7 Examples are “the IID assumption” or “my problem is a DNF on a small number of bits”. [sent-9, score-0.047]

8 One difficulty with any use of the word “assumption” is that you often encounter “if assumption then conclusion so if not assumption then not conclusion “. [sent-11, score-2.082]

9 For example, with variant (1), “the assumption of my prior is not met so the algorithm will not learn”. [sent-13, score-1.067]

10 Or, with variant (3), “the data is not IID, so my learning algorithm designed for IID data will not work”. [sent-14, score-0.464]

11 In each of these cases “will” must be replaced with “may” for correctness. [sent-15, score-0.216]


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