User:IssaRice/Logical induction notation: Difference between revisions

From Machinelearning
No edit summary
No edit summary
Line 16: Line 16:
:<math>\underbrace{\left[(\neg\neg\phi)^{*5} -\phi^{*5}\right]}_{\xi_1} \cdot (\phi - \phi^{*5}) + \underbrace{\left[\phi^{*5} - (\neg \neg \phi)^{*5}\right]}_{\xi_2} \cdot \left(\neg\neg\phi - (\neg\neg\phi)^{*5}\right)</math>
:<math>\underbrace{\left[(\neg\neg\phi)^{*5} -\phi^{*5}\right]}_{\xi_1} \cdot (\phi - \phi^{*5}) + \underbrace{\left[\phi^{*5} - (\neg \neg \phi)^{*5}\right]}_{\xi_2} \cdot \left(\neg\neg\phi - (\neg\neg\phi)^{*5}\right)</math>


Since the coefficients (<math>\xi_1</math> and <math>\xi_2</math>) are in <math>\mathcal F_5</math>, this is an <math>\mathcal{E\!F}_5</math>-combination. Let's call this 5-strategy <math>T_5</math>. We can pick out the coefficient for the <math>\phi</math> term like <math>T_5[\phi] = (\neg\neg\phi)^{*5} -\phi^{*5}</math>. But since each coefficient is a feature (which is a function), we can also apply each coefficient to some valuation sequence <math>\overline{\mathbb V}</math>, like this:
Since the coefficients (<math>\xi_1</math> and <math>\xi_2</math>) are in <math>\mathcal{E\!F}_5</math>, this is an <math>\mathcal{E\!F}_5</math>-combination. Let's call this 5-strategy <math>T_5</math>. We can pick out the coefficient for the <math>\phi</math> term like <math>T_5[\phi] = (\neg\neg\phi)^{*5} -\phi^{*5}</math>. But since each coefficient is a feature (which is a function), we can also apply each coefficient to some valuation sequence <math>\overline{\mathbb V}</math>, like this:


:<math>T_5(\overline{\mathbb V}) = \left[(\neg\neg\phi)^{*5}(\overline{\mathbb V}) -\phi^{*5}(\overline{\mathbb V})\right] \cdot (\phi - \phi^{*5}(\overline{\mathbb V})) + \left[\phi^{*5}(\overline{\mathbb V}) - (\neg \neg \phi)^{*5}(\overline{\mathbb V})\right] \cdot \left(\neg\neg\phi - (\neg\neg\phi)^{*5}(\overline{\mathbb V})\right)</math>
:<math>T_5(\overline{\mathbb V}) = \left[(\neg\neg\phi)^{*5}(\overline{\mathbb V}) -\phi^{*5}(\overline{\mathbb V})\right] \cdot (\phi - \phi^{*5}(\overline{\mathbb V})) + \left[\phi^{*5}(\overline{\mathbb V}) - (\neg \neg \phi)^{*5}(\overline{\mathbb V})\right] \cdot \left(\neg\neg\phi - (\neg\neg\phi)^{*5}(\overline{\mathbb V})\right)</math>

Revision as of 02:50, 3 August 2018

This is in user space because it's not really about machine learning.

Term Notation Type Definition Notes
-combination Function application of an -combination uses square brackets instead of parentheses. Why? As far as I can tell, this is because each coefficient is in so is itself a function. This means we have two senses of "application": we can pick out the specific coefficient we want (square brackets), or we can apply each coefficient to return something (parentheses).
Holdings from against (a -combination)
Trading strategy

Example of a 5-strategy given on p. 18 of the paper:

Since the coefficients ( and ) are in , this is an -combination. Let's call this 5-strategy . We can pick out the coefficient for the term like . But since each coefficient is a feature (which is a function), we can also apply each coefficient to some valuation sequence , like this:

Now each coefficient is a real number, so is an -combination. Note that since is a function that takes a sentence or the number and is a valuation sequence (not a sentence or number), there appears to be a type error in writing . What is going on is that we aren't evaluating at ; rather, we are evaluating each coefficient of , to convert the range of from to .

External links