Search results
Results From The WOW.Com Content Network
Sparse dictionary learning (also known as sparse coding or SDL) is a representation learning method which aims to find a sparse representation of the input data in the form of a linear combination of basic elements as well as those basic elements themselves. These elements are called atoms, and they compose a dictionary.
In this example, we will consider a dictionary consisting of the following words: {a, ab, bab, bc, bca, c, caa}. The graph below is the Aho–Corasick data structure constructed from the specified dictionary, with each row in the table representing a node in the trie, with the column path indicating the (unique) sequence of characters from the root to the node.
This means the cost of going from source to u via w has the cost of at least dist[w] + the minimal cost of going from w to u. As the edge costs are positive, the minimal cost of going from w to u is a positive number. However, dist[u] is at most dist[w] because otherwise w would have been picked by the priority queue instead of u.
The "QMap" key/value dictionary (up to Qt 4) template class of Qt is implemented with skip lists. [13] Redis, an ANSI-C open-source persistent key/value store for Posix systems, uses skip lists in its implementation of ordered sets. [14] Discord uses skip lists to handle storing and updating the list of members in a server. [15]
The Python programming language, which contains many Monty Python references as feature names, has an internal-only module called "Argument Clinic" to pre-process Python files. [17] [18] The sketch is referenced in a line of dialogue in the TV show House, season 6, episode 10, "Wilson". The character Dr. Wilson says to Dr. House "I didn't come ...
In computer science, a trie (/ ˈ t r aɪ /, / ˈ t r iː /), also known as a digital tree or prefix tree, [1] is a specialized search tree data structure used to store and retrieve strings from a dictionary or set.
Graphs occur frequently in everyday applications. Examples include biological or social networks, which contain hundreds, thousands and even billions of nodes in some cases (e.g. Facebook or LinkedIn).
Edit distance matrix for two words using cost of substitution as 1 and cost of deletion or insertion as 0.5. For example, the Levenshtein distance between "kitten" and "sitting" is 3, since the following 3 edits change one into the other, and there is no way to do it with fewer than 3 edits: kitten → sitten (substitution of "s" for "k"),