Stack Search

	Stack Search Stack search (also known as Stack decoding algorithm) is a search algorithm similar to beam search. It can be used to explore tree-structured search spaces and is often employed in Natural language processing applications, such as parsing of natural languages, or for decoding of error correcting codes where the technique goes under the name of sequential decoding Recognised by John Wozencraft, sequential decoding is a limited memory technique for decoding tree codes. Sequential decoding is mainly used as an approximate decoding algorithm for long constraint-length convolutional codes. This approach may n .... Stack search keeps a list of the best ''n'' candidates seen so far. These candidates are incomplete solutions to the search problems, e.g. partial parse trees. It then iteratively expands the best partial solution, putting all resulting partial solutions onto the stack and then trimming the resulting list of partial solutions to the top ''n'' candidates, until a real solut ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu]
	Beam Search In computer science, beam search is a heuristic search algorithm that explores a graph by expanding the most promising node in a limited set. Beam search is an optimization of best-first search that reduces its memory requirements. Best-first search is a graph search which orders all partial solutions (states) according to some heuristic. But in beam search, only a predetermined number of best partial solutions are kept as candidates. It is thus a greedy algorithm. The term "beam search" was coined by Raj Reddy of Carnegie Mellon University in 1977. Details Beam search uses breadth-first search to build its search tree. At each level of the tree, it generates all successors of the states at the current level, sorting them in increasing order of heuristic cost. However, it only stores a predetermined number, \beta, of best states at each level (called the beam width). Only those states are expanded next. The greater the beam width, the fewer states are pruned. With an infinite ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu]
picture info	Natural Language Processing Natural language processing (NLP) is an interdisciplinary subfield of linguistics, computer science, and artificial intelligence concerned with the interactions between computers and human language, in particular how to program computers to process and analyze large amounts of natural language data. The goal is a computer capable of "understanding" the contents of documents, including the contextual nuances of the language within them. The technology can then accurately extract information and insights contained in the documents as well as categorize and organize the documents themselves. Challenges in natural language processing frequently involve speech recognition, natural-language understanding, and natural-language generation. History Natural language processing has its roots in the 1950s. Already in 1950, Alan Turing published an article titled "Computing Machinery and Intelligence" which proposed what is now called the Turing test as a criterion of intelligence, t ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu]
picture info	Error Correcting Code In computing, telecommunication, information theory, and coding theory, an error correction code, sometimes error correcting code, (ECC) is used for controlling errors in data over unreliable or noisy communication channels. The central idea is the sender encodes the message with redundant information in the form of an ECC. The redundancy allows the receiver to detect a limited number of errors that may occur anywhere in the message, and often to correct these errors without retransmission. The American mathematician Richard Hamming pioneered this field in the 1940s and invented the first error-correcting code in 1950: the Hamming (7,4) code. ECC contrasts with error detection in that errors that are encountered can be corrected, not simply detected. The advantage is that a system using ECC does not require a reverse channel to request retransmission of data when an error occurs. The downside is that there is a fixed overhead that is added to the message, thereby requiring a h ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu]
	Sequential Decoding Recognised by John Wozencraft, sequential decoding is a limited memory technique for decoding tree codes. Sequential decoding is mainly used as an approximate decoding algorithm for long constraint-length convolutional codes. This approach may not be as accurate as the Viterbi algorithm but can save a substantial amount of computer memory. It was used to decode a convolutional code in 1968 Pioneer 9 mission. Sequential decoding explores the tree code in such a way to try to minimise the computational cost and memory requirements to store the tree. There is a range of sequential decoding approaches based on the choice of metric and algorithm. Metrics include: Fano metric Zigangirov metric Gallager metric Algorithms include: Stack algorithm Fano algorithm Creeper algorithm Fano metric Given a partially explored tree (represented by a set of nodes which are limit of exploration), we would like to know the best node from which to explore further. The Fano metric (named aft ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu]