Auto Topic: hash
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Coverage Score
1
Mentioned Chunks
15
Mentioned Docs
2
Required Dimensions
definitionpros_cons
Covered Dimensions
definitionpros_cons
Keywords
hash
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| Source | Type | Target | W |
|---|---|---|---|
| Auto Topic: hash | CO_OCCURS | Auto Topic: near | 6 |
| Auto Topic: bin | CO_OCCURS | Auto Topic: hash | 4 |
Evidence Chunks
| Source | Confidence | Mentions | Snippet |
|---|---|---|---|
textbook Artificial-Intelligence-A-Modern-Approach-4th-Edition.pdf | 0.67 | 7 | ut how can we find nearest neighbors using a hash table, when hash codes rely on anexact match? Hash codes randomly distribute values among the bins, but we want to have near points grouped together in the same bin; we want a locality-sensitive hash (LSH). Locality-sensitive hash ... |
textbook Artificial-Intelligence-A-Modern-Approach-4th-Edition.pdf | 0.61 | 4 | ... bout 10 dimensions when there are thousands of examples or up to 20 dimensions with millions of examples. 19.7.3 Locality-sensitive hashing Hash tables have the potential to provide even faster lookup than binary trees. But how can we find nearest neighbors using a hash table, whe ... |
textbook Artificial-Intelligence-A-Modern-Approach-4th-Edition.pdf | 0.59 | 3 | near neighbors, we will need a hash function g(x) that has the property that, for any two points x j and x j′, the probability that they have the same hash code is small if their distance is more than cr , and is high if their distance is less than r. For simplicity we will treat ... |
textbook Artificial-Intelligence-A-Modern-Approach-4th-Edition.pdf | 0.59 | 3 | ... ndom subset of the bit-string representation. We choose ℓ different random projections and createℓ hash tables, g1(x),..., gℓ(x). We then enter all the examples into each hash table. Then when given a query point xq, we fetch the set of points in bin gi(xq) of each hash table, an ... |
textbook Artificial-Intelligence-A-Modern-Approach-4th-Edition.pdf | 0.55 | 1 | ... O queue Stack recently added node; we shall see it is used in depth-first search. The reached states can be stored as a lookup table (e.g. a hash table) where each key is a state and each value is the node for that state. 3.3.3 Redundant paths The search tree shown in Figure 3.4 ( ... |
textbook Artificial-Intelligence-A-Modern-Approach-4th-Edition.pdf | 0.55 | 1 | ... GS(x) field picks out the argument list(A,B). Knows facts in one bucket and all the Brother facts in another. The buckets can be stored in a hash table for efficient access. Predicate indexing is useful when there are many predicate symbols but only a few clauses for each symbol. S ... |
textbook Artificial-Intelligence-A-Modern-Approach-4th-Edition.pdf | 0.55 | 1 | ... e bucket. For this particular query, it would help if facts were indexed both by predicate and by second argument, perhaps using a combined hash table key. Then we could simply construct the key from the query and retrieve exactly those facts that unify with the query. For other ... |
textbook Artificial-Intelligence-A-Modern-Approach-4th-Edition.pdf | 0.55 | 1 | ... But instance-based methods are designed for large data sets, so we would like something faster. The next two subsections show how trees and hash tables can be used to speed up the computation. 19.7.2 Finding nearest neighbors with k-d trees A balanced binary tree over data with a ... |
textbook Artificial-Intelligence-A-Modern-Approach-4th-Edition.pdf | 0.55 | 1 | et of points in bin gi(xq) of each hash table, and union these ℓ sets together into a set of candidate points, C. Then we compute the actual distance to xq for each of the points in C and return the k closest points. With high probability, each of the points that are near to xq w ... |
textbook Artificial-Intelligence-A-Modern-Approach-4th-Edition.pdf | 0.55 | 1 | ... return Ai function CONSISTENT -D ET?(A, E) returns a truth value inputs: A, a set of attributes E, a set of examples local variables: H, a hash table for each example e in E do if some example in H has the same values as e for the attributes A but a different classificationthen re ... |
textbook Artificial-Intelligence-A-Modern-Approach-4th-Edition.pdf | 0.55 | 1 | ... and Schaeffer, J. (2008). Coarse-to-fine search techniques. Tech. rep., University of Alberta. Andoni, A. and Indyk, P. (2006). Near-optimal hash- ing algorithms for approximate nearest neighbor in high dimensions. In FOCS-06. Andor, D., Alberti, C., Weiss, D., Severyn, A., Presta ... |
textbook Artificial-Intelligence-A-Modern-Approach-4th-Edition.pdf | 0.55 | 1 | ... local-sensing vacuum world, 148 local beam search, 133 local consistency, 170 localist representation, 77 locality, 257 locality-sensitive hash (LSH), 707 localization, 151, 494, 939 Markov, 984 locally structured system, 435 locally weighted regression, 709 local search, 128–13 ... |
textbook Artificial-Intelligence-A-Modern-Approach-4th-Edition.pdf | 0.55 | 1 | ... , 1104, 1116 LQR (linear quadratic regulator), 962, 982 LRTA*-AGENT , 158 LRTA*-COST, 158 LRTA*, 157, 163, 383, 570 LSH (locality-sensitive hash), 707 LSTM (long short-term memory), 826, 914 LSVRC, 46 LT (Logic Theorist), 36, 266 Lu, C., 872, 1117 Lu, F., 984, 1104 Lu, P., 223, 7 ... |
quizzes Quizes/Week 1 Python Skills With Answers.pdf | 0.55 | 1 | ... tion The following is valid Python code. >>> my_tuple = (4, 1, 2) >>> my_tuple[0] = 3 True False 4 True or False 1 point Question A hash function takes a message of fixed length and generates a code of variable length. True False 5 |
quizzes Quizes/Week 1 Python Skills With Answers.pdf | 0.55 | 1 | on code. >>> my_tuple = (4, 1, 2) >>> my_tuple[0] = 3 True False 4 True or False 1 point Question A hash function takes a message of fixed length and generates a code of variable length. True False 5 True or False 1 point Question Some mathematical operations can be performed on ... |