PDF-Bigram approximation

Interpolated Trigram Model Where observations and ends in state sj 9. . of Edit Distance. Robert Krauthgamer, . Weizmann Institute of Science. SPIRE 2013. TexPoint. fonts used in EMF. . Read the . TexPoint. manual before you delete this box. .: . A. A. A. A. A. A. A. Raja . Giryes. ICASSP 2011. Volkan. Cevher. Agenda. The sparse approximation problem. Algorithms and pre-run guarantees. Online performance guarantees. Performance bound. Parameter selection. 2. Sparse approximation. Actual area under curve:. Left-hand rectangular approximation:. Approximate area:. (too low). Approximate area:. Right-hand rectangular approximation:. (too high). Averaging the two:. 1.25% error. (too high). Sometimes we can handle NP problems with polynomial time algorithms which are guaranteed to return a solution within some specific bound of the optimal solution. within a constant . c. . of the optimal. Algorithms. and Networks 2014/2015. Hans L. . Bodlaender. Johan M. M. van Rooij. C-approximation. Optimization problem: output has a value that we want to . maximize . or . minimize. An algorithm A is an . A Mini-Survey. Chandra . Chekuri. Univ. of Illinois, Urbana-Champaign. Submodular Set Functions. A function . f. : 2. N. . . . R . is submodular if. . f(A. ) + . f(B. ) ≥ . f(A. . B. ) + . Problem. Yan Lu. 2011-04-26. Klaus Jansen SODA 2009. CPSC669 Term Project—Paper Reading. 1. Problem Definition. 2. Approximation Scheme. 2.1 Instances with similar capacities. 2.2 General cases . Outline. δ. -Timeliness. Carole . Delporte-Gallet. , . LIAFA . UMR 7089. , Paris VII. Stéphane Devismes. , VERIMAG UMR 5104, Grenoble I. Hugues Fauconnier. , . LIAFA . UMR 7089. , Paris VII. LIAFA. Motivation. Julia Chuzhoy. Toyota Technological Institute at Chicago. Routing Problems. Input. : Graph G, source-sink pairs (s. 1. ,t. 1. ),…,(. s. k. ,t. k. ).. Goal. : Route as many pairs as possible; minimize edge congestion.. Method used to encode information in terms of signal transition. The original binary information is recovered simply by comparing the polarity of adjacent binary symbols to establish whether or not a transition has occured. *University . of California . Berkeley. Mohsen Imani, . Abbas . Rahimi. *. , . Tajana S. Rosing. Resistive Configurable Associative Memory . for Approximate . Computing. Motivation. 2. Internet of Things. How accurate is your estimate?. Differential Notation. The Linear Approximation to . y. = . f. (. x. ) is often written using the “differentials” . dx. and . dy. . In this notation, . dx. is used instead of . Stochastic . Optimization. Anupam Gupta. Carnegie Mellon University. IPCO Summer . School. Approximation . Algorithms for. Multi-Stage Stochastic Optimization. {vertex cover, . S. teiner tree, MSTs}. EECT 7327 . Fall 2014. Successive Approximation. (SA) ADC. Successive Approximation ADC. – . 2. –. Data Converters Successive Approximation ADC Professor Y. Chiu. EECT 7327 . Fall 2014. Binary search algorithm → N*.