V. Clustering
... in text analysis(1/2) Cluster hypothesis “Relevant documents tend to be more similar to each other than to nonrelevant ones.” If cluster hypothesis holds for a particular document collection, then the clustering of documents may help to improve the search effectiveness. • Improving search recall ...
... in text analysis(1/2) Cluster hypothesis “Relevant documents tend to be more similar to each other than to nonrelevant ones.” If cluster hypothesis holds for a particular document collection, then the clustering of documents may help to improve the search effectiveness. • Improving search recall ...
3. Keyword Cover Search Module
... Recently, the spatial keyword search has received considerable attention from research community. Some existing works focus on retrieving individual objects by specifying a query consisting of a query location and a set of query keywords (or known as document in some context). Each retrieved object ...
... Recently, the spatial keyword search has received considerable attention from research community. Some existing works focus on retrieving individual objects by specifying a query consisting of a query location and a set of query keywords (or known as document in some context). Each retrieved object ...
Full text
... composition whose leftmost digit is one is cast into set A; those whose leftmost digit is two are cast into set B; and those whose leftmost digit is three are cast into set C. Descending the list, we then call the first A9 Al9 the second A, A2, . . . , the first B, Si, the second B, B2, and so on. W ...
... composition whose leftmost digit is one is cast into set A; those whose leftmost digit is two are cast into set B; and those whose leftmost digit is three are cast into set C. Descending the list, we then call the first A9 Al9 the second A, A2, . . . , the first B, Si, the second B, B2, and so on. W ...
Cones on homotopy probability spaces
... Now let V → CV → C be an algebraic cone on V . Because the first map is an algebra map, Proposition 3.4 applies and the cumulant of X in V equals the cumulant of X̃ : Cn → V → CV . Thus, the classical cumulants can be computed within the cone CV . Proposition 3.2 applies to the second map of V → CV ...
... Now let V → CV → C be an algebraic cone on V . Because the first map is an algebra map, Proposition 3.4 applies and the cumulant of X in V equals the cumulant of X̃ : Cn → V → CV . Thus, the classical cumulants can be computed within the cone CV . Proposition 3.2 applies to the second map of V → CV ...
CS 332: Algorithms
... ● Can we prove it will work? ● Sketch of an inductive argument (induction on ...
... ● Can we prove it will work? ● Sketch of an inductive argument (induction on ...
Random Walk With Continuously Smoothed Variable Weights
... Smoothing reduces the effect of the earlier search history, placing more emphasis on recent events and adapting the search heuristics to the current search space topology. Our smoothing technique does this as follows. Associate with each variable v a weight wv , initialised to 0 and updated each tim ...
... Smoothing reduces the effect of the earlier search history, placing more emphasis on recent events and adapting the search heuristics to the current search space topology. Our smoothing technique does this as follows. Associate with each variable v a weight wv , initialised to 0 and updated each tim ...
2 Basic notions: infinite dimension
... Y is still defined on (0, 1). This is known as the isomorphism theorem for measures (or measure spaces, or probability spaces). See also [7], Sect. 2.4 and 2.7; [4], Example 1 and Prop. 6; [8], Theorems 2-3 and 4-3; [9], Theorem 3.4.23. The corresponding result for measurable spaces is deeper;1 inde ...
... Y is still defined on (0, 1). This is known as the isomorphism theorem for measures (or measure spaces, or probability spaces). See also [7], Sect. 2.4 and 2.7; [4], Example 1 and Prop. 6; [8], Theorems 2-3 and 4-3; [9], Theorem 3.4.23. The corresponding result for measurable spaces is deeper;1 inde ...
Checking Polynomial Identities over any Field: Towards a
... elements in it, then there is no set S large enough to be used in the algorithm. In this case, S can be selected from an extension field of F and P is evaluated over the extension field. Clearly if P is the zero polynomial, the test always outputs ‘probably zero’which is the correct answer. On the o ...
... elements in it, then there is no set S large enough to be used in the algorithm. In this case, S can be selected from an extension field of F and P is evaluated over the extension field. Clearly if P is the zero polynomial, the test always outputs ‘probably zero’which is the correct answer. On the o ...
Delaunay graphs of point sets in the plane with respect to axis
... D(P ′ ). In our arguments about Delaunay graphs of randomly selected point sets in the square, it will be convenient to consider the graph D(π) for a random permutation π. The number of edges of D(π) will be denoted by |D(π)| and log denotes the natural logarithm. Lemma 6. Let π : {1, 2, . . . , n} ...
... D(P ′ ). In our arguments about Delaunay graphs of randomly selected point sets in the square, it will be convenient to consider the graph D(π) for a random permutation π. The number of edges of D(π) will be denoted by |D(π)| and log denotes the natural logarithm. Lemma 6. Let π : {1, 2, . . . , n} ...
Fisher–Yates shuffle
The Fisher–Yates shuffle (named after Ronald Fisher and Frank Yates), also known as the Knuth shuffle (after Donald Knuth), is an algorithm for generating a random permutation of a finite set—in plain terms, for randomly shuffling the set. A variant of the Fisher–Yates shuffle, known as Sattolo's algorithm, may be used to generate random cyclic permutations of length n instead. The Fisher–Yates shuffle is unbiased, so that every permutation is equally likely. The modern version of the algorithm is also rather efficient, requiring only time proportional to the number of items being shuffled and no additional storage space.Fisher–Yates shuffling is similar to randomly picking numbered tickets (combinatorics: distinguishable objects) out of a hat without replacement until there are none left.