Auto Topic: patrons
auto_patrons | topic
Coverage Score
1
Mentioned Chunks
17
Mentioned Docs
1
Required Dimensions
definitionpros_cons
Covered Dimensions
definitionpros_cons
Keywords
patrons
Relations
| Source | Type | Target | W |
|---|---|---|---|
| Auto Topic: patrons | CO_OCCURS | Propositional Logic | 5 |
Evidence Chunks
| Source | Confidence | Mentions | Snippet |
|---|---|---|---|
textbook Artificial-Intelligence-A-Modern-Approach-4th-Edition.pdf | 0.63 | 5 | ... extra condition that will rule out X2, while continuing to classify X1 as positive. One possibility is h2 : ∀x WillWait(x) ⇔ Alternate(x) ∧ Patrons(x,Some). • The third example, X3, is positive. h2 predicts it to be negative, so it is a false negative. Therefore, we need to gener ... |
textbook Artificial-Intelligence-A-Modern-Approach-4th-Edition.pdf | 0.61 | 4 | n? Bar? Raining? Alternate? Patrons? Fri/Sat? No Yes No Yes Yes Yes No Ye s No Yes Yes No Yes No Ye s YesNo WaitEstimate? Figure 19.3 A decision tree for deciding whether to wait for a table. Section 19.3 Learning Decision Trees 677 (a) None Some Full Patrons? YesNo Hungry? (b) N ... |
textbook Artificial-Intelligence-A-Modern-Approach-4th-Edition.pdf | 0.61 | 4 | ... the tree in Figure 19.6 (page 678) expresses the following logical definition (which we will call hr for future reference): ∀r WillWait(r) ⇔ Patrons(r,Some) ∨ Patrons(r,Full) ∧ Hungry(r) ∧ Type(r,French) ∨ Patrons(r,Full) ∧ Hungry(r) ∧ Type(r,Thai) ∧ Fri/Sat(r) ∨ Patrons(r,Full) ∧ ... |
textbook Artificial-Intelligence-A-Modern-Approach-4th-Edition.pdf | 0.59 | 3 | ... act Gain(A) is just what we need to implement the I MPORTANCE function. Returning to the attributes considered in Figure 19.4, we have Gain(Patrons) = 1 − [ 2 12B( 0 2 ) + 4 12B( 4 4 ) + 6 12B( 2 6 ) ] ≈ 0.541 bits, Gain(Type) = 1 − [ 2 12B( 1 2 ) + 2 12B( 1 2 ) + 4 12B( 2 4 ) + ... |
textbook Artificial-Intelligence-A-Modern-Approach-4th-Edition.pdf | 0.57 | 2 | ... t 1−ϵ ≤ e−ϵ, we can achieve this if we allow the algorithm to see N ≥ 1 ϵ ( ln 1 δ + ln |H| ) (19.1) 692 Chapter 19 Learning from Examples Patrons(x, Some) No Yes Yes No Patrons(x, Full) Fri/Sat(x) Yes No Yes ^ Figure 19.10 A decision list for the restaurant problem. examples. Th ... |
textbook Artificial-Intelligence-A-Modern-Approach-4th-Edition.pdf | 0.57 | 2 | ... n contrast, the individual tests are more complex. Figure 19.10 shows a decision list that represents the following hypothesis: WillWait ⇔ (Patrons= Some) ∨ (Patrons= Full ∧ Fri/Sat). If we allow tests of arbitrary size, then decision lists can represent any Boolean function Sect ... |
textbook Artificial-Intelligence-A-Modern-Approach-4th-Edition.pdf | 0.57 | 2 | ... s logically implied by C2. This is easily done. For example, if C2(x) is Alternate(x) ∧ Section 20.1 A Logical Formulation of Learning 743 Patrons(x,Some), then one possible generalization is given by C1(x) ≡ Patrons(x,Some). This is called dropping conditions. Intuitively, it ge ... |
textbook Artificial-Intelligence-A-Modern-Approach-4th-Edition.pdf | 0.57 | 2 | ... ies consistent with the first four examples; here are two of them: h′ 4 : ∀x WillWait(x) ⇔ ¬ WaitEstimate(x,30-60). h′′ 4 : ∀x WillWait(x) ⇔ Patrons(x,Some) ∨ (Patrons(x,Full) ∧ WaitEstimate(x,10-30)). The CURRENT -BEST-LEARNING algorithm is described nondeterministically, because ... |
textbook Artificial-Intelligence-A-Modern-Approach-4th-Edition.pdf | 0.55 | 1 | ... restaurant has a comfortable bar area to wait in. 3. Fri/Sat: true on Fridays and Saturdays. 4. Hungry: whether we are hungry right now. 5. Patrons: how many people are in the restaurant (values are None, Some, and Full). 6. Price: the restaurant’s price range ($, $$, $$$). 7. Ra ... |
textbook Artificial-Intelligence-A-Modern-Approach-4th-Edition.pdf | 0.55 | 1 | ... the decision function that SR uses for the restaurant problem is shown in Figure 19.3. Following the branches, we see that an example with Patrons=Full and WaitEstimate=0–10 will be classified as positive (i.e., yes, we will wait for a table). 19.3.1 Expressiveness of decision tr ... |
textbook Artificial-Intelligence-A-Modern-Approach-4th-Edition.pdf | 0.55 | 1 | ... No Ye s No Ye s No Ye s No Ye s None Some Full >60 30-60 10-30 0-10 No Ye s Alternate? Hungry? Reservation? Bar? Raining? Alternate? Patrons? Fri/Sat? No Yes No Yes Yes Yes No Ye s No Yes Yes No Yes No Ye s YesNo WaitEstimate? Figure 19.3 A decision tree for deciding whether to ... |
textbook Artificial-Intelligence-A-Modern-Approach-4th-Edition.pdf | 0.55 | 1 | ... .3.3. The function P LURALITY -VALUE selects the most common output value among a set of examples, breaking ties randomly. None Some Full Patrons? No Yes No Yes Hungry? No No Yes Fri/Sat? Yes No Yes Type? French Italian Thai Burger Yes No Figure 19.6 The |
textbook Artificial-Intelligence-A-Modern-Approach-4th-Edition.pdf | 0.55 | 1 | put value among a set of examples, breaking ties randomly. None Some Full Patrons? No Yes No Yes Hungry? No No Yes Fri/Sat? Yes No Yes Type? French Italian Thai Burger Yes No Figure 19.6 The decision tree induced from the 12-example training set. ure 19.3. One might conclude that ... |
textbook Artificial-Intelligence-A-Modern-Approach-4th-Edition.pdf | 0.55 | 1 | ... act Gain(A) is just what we need to implement the I MPORTANCE function. Returning to the attributes considered in Figure 19.4, we have Gain(Patrons) = 1 − [ 2 12B( |
textbook Artificial-Intelligence-A-Modern-Approach-4th-Edition.pdf | 0.55 | 1 | ... ange when the input changes. 18 Decision tree models are consid- ered to be highly interpretable; we can understand that following the path Patrons=Full and WaitEstimate=0–10 in a decision tree leads to a decision to wait. A decision tree is inter- pretable for two reasons. First ... |
textbook Artificial-Intelligence-A-Modern-Approach-4th-Edition.pdf | 0.55 | 1 | stand that following the path Patrons=Full and WaitEstimate=0–10 in a decision tree leads to a decision to wait. A decision tree is inter- pretable for two reasons. First, we humans have experience in understanding IF/THEN rules. (In contrast, it is very difficult for humans to ge ... |
textbook Artificial-Intelligence-A-Modern-Approach-4th-Edition.pdf | 0.55 | 1 | ... hesis, if the hypothesis says it shouldFalse negative be negative but in fact it is positive. For instance, the new exampleX13 described by Patrons(X13,Full) ∧ ¬Hungry(X13) ∧... ∧ WillWait(X13) would be a false negative for the hypothesis hr given earlier. From hr and the example ... |