Consider Slide 55 in "8 Reinforcement Learning.ppt". We have a part of another trajectory: (c1,E, -1) -> (c2, N, -1). 。 Under SARSA, the new Q(c1,E) = • Under Q-Learning, the new Q(c1,E) = answer) Picture below is of the slide N(s,a) (keep one decimal value in your answer) (keep one decimal value in your Q(s, a) 6 3 1 -2.4 -1.8 41.0 10 10 55 12 0 25 25 +100 C-2.4 16.4-1.8 60.8 0.0 88.8 +100 11 3 1 -2.4 -1.8 3.4 18 5 13 9 -2.9 45.8 9 55 55 2 -100 b-2.9 -2.9 12.2 -101.0 -100 4 -2.9 -51.5 6 4 28 4 -2.6 -1.9 11.0 -101.0 00 8 17 3 20 1 88 8a-2.5 -2.2-2.0 0.5 -2.0 -2.1 -15.0 -14.9 6 4 4 77 -2.4 -2.1 -1.9 -3.7 1 2 3 4 1 2 3 4

Consider Slide 55 in "8 Reinforcement Learning.ppt". We have a part of another trajectory: (c1,E, -1) -> (c2, N, -1). 。 Under SARSA, the new Q(c1,E) = • Under Q-Learning, the new Q(c1,E) = answer) Picture below is of the slide N(s,a) (keep one decimal value in your answer) (keep one decimal value in your Q(s, a) 6 3 1 -2.4 -1.8 41.0 10 10 55 12 0 25 25 +100 C-2.4 16.4-1.8 60.8 0.0 88.8 +100 11 3 1 -2.4 -1.8 3.4 18 5 13 9 -2.9 45.8 9 55 55 2 -100 b-2.9 -2.9 12.2 -101.0 -100 4 -2.9 -51.5 6 4 28 4 -2.6 -1.9 11.0 -101.0 00 8 17 3 20 1 88 8a-2.5 -2.2-2.0 0.5 -2.0 -2.1 -15.0 -14.9 6 4 4 77 -2.4 -2.1 -1.9 -3.7 1 2 3 4 1 2 3 4

Database System Concepts

7th Edition

ISBN:9780078022159

Author:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan

Publisher:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan

Chapter1: Introduction

Section: Chapter Questions

Problem 1PE

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Which of the following are equivalent to:¬((∀x)(∃y)(∀z)(P(x)→(Q(y)∨R(z))))? (Select all that apply.) Group of answer choices (∃x)(¬(∃y)(∀z)(P(x)→(Q(y)∨R(z)))) (∃x)(∀y)(¬(∀z)(P(x)→(Q(y)∨R(z)))) (∃x)(∀y)(∃z)(¬(P(x)→(Q(y)∨R(z)))) (∃x)(∀y)(∃z)(P(x)∧¬(Q(y)∨R(z))))
There are four questions related to the same scenario in the test presented in random order. Consider a classification problem of a online customer as follows Predicted (output) variable is buy food or not (has two distinct values as yes and no). Input variables are: total food spending last year, age of the customer, categorical (3 levels): (young, middle, old) yearly income of customer, categorical (5 levels): (low, middle_low, middle, high_middle, high) marital status of the customer (marital status has four distinct values), customers occupation, (occupation has fife distinct values), Thermometer encoding is used properly. This problem is to be solved by multilayer feedforward neural network with a single hidden layer containing 5 nodes. What is the total number of nodes? а. 24 O b. 11 О. 22 O d. 20
In this version of the Monty Hall problem, there are 4 doors with a car behind one of them and goats behind the other 3. You pick a door. Monty then shows you two doors that have goats behind them. He then asks you if you want to stay with your original pick or switch to the unopened door. Let A be the event that you win the car. Let B be the event that you stay with your original pick. What is p(AB)? OOOOO 1/4 1/3 1/2 2/3 3/4
Suppose we are trying to model a classification problem with two labels: 'sick' and 'healthy.' For the purpose of this test, we consider a positive result to be testing sick and a negative to test as healthy. After fitting the model with LDA in R, we compare predicted values from the actual values, as shown below: > lda. fit = 1da (test.result x1 + x2, data-mydata) > lda. pred > lda.class=lda.pred$class > table (lda.class, mydata$test.result) = predict (1da.fit) mydata lda.pred sick healthy sick 40 healthy 25 32 121
Question 48. Let us return to the Titanic data set. We now have learned several models and want to choose the best one. We used three different methods to validate these models: The training error rate (apparent error rate), the error rate on an external test set and the error rate estimated by a 10-fold cross validation. Training Error | Error on the test set | Cross Validation Error 0.18 Learner Decision Tree 0.22 0.21 Random Forest 0.01 0.10 0.12 1-Nearest-Neighbour 0.18 0.19 Which of the following statements are correct? a) 1-Nearest-Neighbour has a perfect training error and hence it should be used here. b) Random Forests outperforms both 1-Nearest-Neighbour and the Decision Tree in terms of prediction error. c) Not just in this case, but in general, Cross Validation is the better validation strategy and should always be preferred over the error on a single test set. d) Not just in this case, but in general, Decision Trees always perform worse than Random Forests.
I have been confused for a while on worst case, best case, and average case for algorithms. I get that big O noation is worst case, Omega notation is best case, and theta is average notation, but I don't get how are we supposed to be able to tell what the function (ex. (n), (log n), ( n log n), etc.) is for those algorithms and why are they those functions? I got an AI generated response earlier, which was helpful in pointing out what kinds of algorithms might have (n) or (log n), but what I really want to know is how can I tell if a function is going to specifically be (log n) vs (n log n), (n), etc., why is it that function and which functions (log n) (n), (n log n), are better or faster than others.
A valid argument is given below. Fill in the missing values of the proof. vx (R(x) v Q(x)) ax (P(x) v -Q(x)) 1. ax (P(x) v -Q(x)) Hypothesis 2. (b is a particular element) ^ (P(b) v ¬Q(b)) (A) : 3x (P(x) v R(x)) 3. P(b) v -Q(b) (B) 4. vx (R(x) v Q(x)) Hypothesis 5. b is a particular element (C) 6. (D) Universal instantiation, 4, 5 7. P(b) v R(b) Resolution, 3, 6 8. 3x (P(x) v R(X)) Existential generalization, 5, 7 (A) Pick (C) Pick (B) Pick (D) Pick Check Next
Consider Slide 55 in "8 Reinforcement Learning.ppt". We have a part of another trajectory: (c1,E, -1) -> (c2, N, -1). 。 Under SARSA, the new Q(c1,E) = • Under Q-Learning, the new Q(c1,E) = answer) Picture below is of the slide N(s,a) (keep one decimal value in your answer) (keep one decimal value in your Q(s, a) 6 3 1 -2.4 -1.8 41.0 10 10 55 12 0 25 25 +100 C-2.4 16.4-1.8 60.8 0.0 88.8 +100 11 3 1 -2.4 -1.8 3.4 18 5 13 9 -2.9 45.8 9 55 55 2 -100 b-2.9 -2.9 12.2 -101.0 -100 4 -2.9 -51.5 6 4 28 4 -2.6 -1.9 11.0 -101.0 00 8 17 3 20 1 88 8a-2.5 -2.2-2.0 0.5 -2.0 -2.1 -15.0 -14.9 6 4 4 77 -2.4 -2.1 -1.9 -3.7 1 2 3 4 1 2 3 4
A Moving to another question will save this response. Question 7 Given that F(A,B,C) = E(0,1,4,5,6), which of the following correctly describes F'? a. F' = E (1, 4, 6, 7) b. F' = T (2, 3, 7) cF=π ( , 1, 4,5 , 7 ) d.F' -Σ2, 3 , 7 ) O a. a O b.b O C.C O d.d A Moving to another question will save this response. 36°C juaiio ENG
Why are event delegation and its components so popular in GUI design? How would you respond if you had relevant illustrations?
Fitting data via a polynomial can be done using a learning agent that minimizes the learning criteria. Show the learning approach to fit 10 data points with a 3rd-degree polynomial and the error function that is being minimized.