Auto Topic: entropy
auto_entropy | topic
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| Source | Type | Target | W |
|---|---|---|---|
| Auto Topic: entropy | CO_OCCURS | Propositional Logic | 10 |
| Auto Topic: entropy | CO_OCCURS | Constraint Satisfaction Problem | 6 |
| Auto Topic: entropy | CO_OCCURS | Informed Search | 3 |
Evidence Chunks
| Source | Confidence | Mentions | Snippet |
|---|---|---|---|
textbook Artificial-Intelligence-A-Modern-Approach-4th-Edition.pdf | 0.67 | 7 | s we’ll be wrong only 1% of the time—so we would like it to have an entropy measure that is close to zero, but positive. In general, the entropy of a random variableV with values vk having probability 680 Chapter 19 Learning from Examples P(vk) is defined as Entropy: H(V ) = ∑ k P ... |
textbook Artificial-Intelligence-A-Modern-Approach-4th-Edition.pdf | 0.65 | 6 | ormation, the less entropy. A random variable with only one possible value—a coin that always comes up heads—has no uncertainty and thus its entropy is defined as zero. A fair coin is equally likely to come up heads or tails when flipped, and we will soon show that this counts as “ ... |
textbook Artificial-Intelligence-A-Modern-Approach-4th-Edition.pdf | 0.65 | 6 | ... .6) Section 22.2 Computation Graphs for Deep Learning 809 In the deep learning literature, it is common to talk about minimizing the cross-entropy Cross-entropy loss. Cross-entropy, written as H(P,Q), is a kind of measure of dissimilarity between two distributions P and Q.2 The g ... |
textbook Artificial-Intelligence-A-Modern-Approach-4th-Edition.pdf | 0.63 | 5 | whole set is H(Output) = B ( p p + n ) . The restaurant training set in Figure 19.2 has p = n = 6, so the corresponding entropy is B(0.5) or exactly 1 bit. The result of a test on an attribute A will give us some information, thus reducing the overall entropy by some amount. We c ... |
textbook Artificial-Intelligence-A-Modern-Approach-4th-Edition.pdf | 0.61 | 4 | ... sing the notion of information gain, which is de- fined in terms ofentropy, which is the fundamental quantity in information theory (Shannon Entropy and Weaver, 1949). Entropy is a measure of the uncertainty of a random variable; the more information, the less entropy. A random va ... |
textbook Artificial-Intelligence-A-Modern-Approach-4th-Edition.pdf | 0.59 | 3 | the allowed set. Nilsson (1986) proposed choosing the max- imum entropy model consistent with the specified constraints. Paskin (2002) developed a “maximum-entropy probabilistic logic” with constraints expressed as weights (relative prob- abilities) attached to first-order clauses. ... |
textbook Artificial-Intelligence-A-Modern-Approach-4th-Edition.pdf | 0.59 | 3 | ... ikelihood) with a fixed-variance Gaus- sian is the same as minimizing squared error. Thus, a linear output layer interpreted in this 2 Cross-entropy is not a distance in the usual sense because H(P,P) is not zero; rather, it equals the entropy H(P). It is easy to show that H(P,Q) ... |
textbook Artificial-Intelligence-A-Modern-Approach-4th-Edition.pdf | 0.57 | 2 | ... artial theory and then “complete” it by picking out one canonical model in the allowed set. Nilsson (1986) proposed choosing the max- imum entropy model consistent with the specified constraints. Paskin (2002) developed a “maximum-entropy probabilistic logic” with constraints |
textbook Artificial-Intelligence-A-Modern-Approach-4th-Edition.pdf | 0.57 | 2 | ... 1961), which was a simulation of human concept learning. ID3 (Quinlan, 1979) added the crucial idea of choosing the attribute with maximum entropy. The concepts of entropy and information theory were developed by Claude Shannon to aid in the study of communication (Shannon and W ... |
textbook Artificial-Intelligence-A-Modern-Approach-4th-Edition.pdf | 0.55 | 1 | ... ones and Love, 2011). The theory of information value was explored first in the context of statistical experi- ments, where a quasi-utility (entropy reduction) was used (Lindley, 1956). The control theo- rist Ruslan Stratonovich (1965) developed the more general theory presented h ... |
textbook Artificial-Intelligence-A-Modern-Approach-4th-Edition.pdf | 0.55 | 1 | ... r the actual data in Equation (22.6) approximates the expectation in Equation (22.7). To minimize the negative log likelihood (or the cross-entropy), we need to be able to interpret the output of the network as a probability. For example, if the network has one output unit with a ... |
textbook Artificial-Intelligence-A-Modern-Approach-4th-Edition.pdf | 0.55 | 1 | ... DKL (P∥Q), where DKL is the Kullback–Leibler divergence, which does satisfy DKL (P∥P) =0. Thus, for fixedP, varying Q to minimize the cross-entropy also minimizes the KL divergence. 810 Chapter 22 Deep Learning way does classical linear regression. The input features to this line ... |
textbook Artificial-Intelligence-A-Modern-Approach-4th-Edition.pdf | 0.55 | 1 | ... m a posteriori hypothesishMAP satisfies hMAP = argmax w P(y |X,W)P(W) = argmin w [−logP(y |X,W) − logP(W)]. The first term is the usual cross-entropy loss; the second term prefers weights that are likely under a prior distribution. This aligns exactly with a regularized loss functi ... |
textbook Artificial-Intelligence-A-Modern-Approach-4th-Edition.pdf | 0.55 | 1 | ... tractability: L = logP(x) − ∫ Q(z)log Q(z) P(z |x)dz = − ∫ Q(z)logQ(z)dz + ∫ Q(z)logP(x)P(z |x)dz = H(Q) +Ez∼Q logP(z,x) where H(Q) is the entropy of the Q distribution. For some variational families Q (such as Gaussian distributions), H(Q) can be evaluated analytically. Moreover ... |
textbook Artificial-Intelligence-A-Modern-Approach-4th-Edition.pdf | 0.55 | 1 | ... 1999) describe compres- sion algorithms for classification, and show the deep connection between the LZW compres- sion algorithm and maximum-entropy language models. Wordnet (Fellbaum, 2001) is a publicly available dictionary of about 100,000 words and phrases, categorized into pa ... |
textbook Artificial-Intelligence-A-Modern-Approach-4th-Edition.pdf | 0.55 | 1 | ximum-entropy language models. Wordnet (Fellbaum, 2001) is a publicly available dictionary of about 100,000 words and phrases, categorized into parts of speech and linked by semantic relations such as synonym, antonym, and part-of. Charniak (1996) and Klein and Manning (2001) dis ... |
textbook Artificial-Intelligence-A-Modern-Approach-4th-Edition.pdf | 0.55 | 1 | ... ), HMMs (Brants, 2000), and logistic regression (Ratnaparkhi, 1996). Historically, a logistic regres- sion model was also called a “maximum entropy Markov model” or MEMM, so some work is under that name. Jurafsky and Martin (2020) have a good chapter on POS tagging. Ng and Jordan ... |
textbook Artificial-Intelligence-A-Modern-Approach-4th-Edition.pdf | 0.55 | 1 | ... thors compare language models by measuring their perplexity. ThePerplexity perplexity of a probability distribution is 2 H, where H is the entropy of the distribution (see Section 19.3.3). A language model with lower perplexity is, all other things being equal, a better model. Bu ... |