mapping filtering and pattern matching

Strings and Pattern Matching Pattern Matching

Strings and Pattern Matching Pattern Matching

Ngày tải lên : 18/09/2013, 21:53
... chuỗi mẫu P T báo if (text letter == pattern letter) so sánh text letter kế với pattern letter kế nh else chuyển pattern dòch sang phải letter until (tìm thấy toàn pattern đến cuối text) Dương Anh ... Strings and Pattern Matching ̈ ̈ Brute Force, Rabin-Karp, Knuth-Morris-Pratt Regular Expressions Dương Anh Đức ... //không khớp, ,nhưng liệu else if j > then //không khớp liệu j f(j-1) //j đến sau matching prefix P j f(j-1) //j đến sau matching prefix P else else i i i + i + return ““Khong co P T” return Khong...
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Tài liệu Báo cáo khoa học: "A Pattern Matching Method for Finding Noun and Proper Noun Translations from Noisy Parallel Corpora" doc

Tài liệu Báo cáo khoa học: "A Pattern Matching Method for Finding Noun and Proper Noun Translations from Noisy Parallel Corpora" doc

Ngày tải lên : 20/02/2014, 22:20
... representation and D T W matching techniques However, we improve on the matching efficiency by installing tagging and statistical filters In addition, we not only obtain a score from the D T W matching ... path, and doing this for all English/Chinese word pairs in the texts The complexity of D T W is @(NM) and the complexity of the matching is O ( I J N M ) where I is the number of nouns and proper ... of their means and standard deviations: E = ~ / i m l - m2) + (~1 - ~2)~ If their Euclidean distance is higher than a certain threshold, we filter the pair out and not use D T W matching on them...
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Báo cáo khoa học: " A simple pattern-matching algorithm for recovering empty nodes and their antecedents∗" pot

Báo cáo khoa học: " A simple pattern-matching algorithm for recovering empty nodes and their antecedents∗" pot

Ngày tải lên : 17/03/2014, 08:20
... kind of pattern we define pattern matching informally as follows If p is a pattern and t is a tree, then p matches t iff t is an extension of p ignoring empty nodes in p For example, the pattern ... -NONE*T*-1 Figure 4: A pattern extracted from the tree displayed in Figure accuracy of transitivity labelling was not systematically evaluated here 2.2 Patterns and matchings Informally, patterns are minimal ... must “standardize apart” or renumber indices appropriately in order to avoid accidentally labelling empty nodes inserted by two independent patterns with the same index Pattern matching and substitution...
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Pattern Matching with egular Expressions R

Pattern Matching with egular Expressions R

Ngày tải lên : 05/10/2013, 13:20
... regular expressions to perform pattern matching and search -and- replace operations In the sections that follow this one, we'll continue the discussion of pattern matching with JavaScript regular ... Methods for Pattern Matching RegExp objects define two methods that perform pattern- matching operations; they behave similarly to the String methods described earlier The main RegExp patternmatching ... searched contains newlines, the ^ and $ anchors match the beginning and end of a line in addition to matching the beginning and end of a string For example, the pattern /Java$/im matches "java"...
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Tài liệu Kalman Filtering and Neural Networks - Chapter 1: KALMAN FILTERS doc

Tài liệu Kalman Filtering and Neural Networks - Chapter 1: KALMAN FILTERS doc

Ngày tải lên : 13/12/2013, 13:15
... Wiley, 1986 [3] M.S Grewal and A.P Andrews, Kalman Filtering: Theory and Practice Englewood Cliffs, NJ: Prentice-Hall, 1993 [4] H.L Van Trees, Detection, Estimation, and Modulation Theory, Part ... where yk is the observable at time k and Hk is the measurement matrix The measurement noise vk is assumed to be additive, white, and Gaussian, with zero mean and with covariance matrix defined by ... scalar random variables; generalization of the theory to vector random variables is a straightforward matter Suppose we are given the observable yk ¼ xk þ vk ; where xk is an unknown signal and vk...
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Tài liệu Kalman Filtering and Neural Networks P2 doc

Tài liệu Kalman Filtering and Neural Networks P2 doc

Ngày tải lên : 14/12/2013, 13:15
... terms of training speed, mapping accuracy, generalization, and overall performance relative to standard backpropagation and related methods Amongst the most promising and enduring of enhanced ... upon driving patterns, and real-world driving patterns are not comprehensively represented by the mandated driving schedules To better assess the emissions that occur in practice and to predict ... enabled the application of feedforward and recurrent neural networks to problems in control, signal processing, and pattern recognition In their work, Singhal and Wu developed a second-order, sequential...
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Tài liệu Kalman Filtering and Neural Networks P3 doc

Tài liệu Kalman Filtering and Neural Networks P3 doc

Ngày tải lên : 14/12/2013, 13:15
...  circle moving right and up; square moving right and down; triangle moving right and up; circle moving right and down; square moving right and up; triangle moving right and down Training was ... Cortex, 1, 1–47 (1991) [2] J.S Lund, Q Wu and J.B Levitt, ‘‘Visual cortex cell types and connections’’, in M.A Arbib, Ed., Handbook of Brain Theory and Neural Networks, Cambridge, MA: MIT Press, ... Lomber, P Girard and J Bullier, ´ ‘‘Cortical feedback improves discrimination between figure and background by V1, V2 and V3 neurons’’, Nature, 394, 784–787 (1998) [4] M.W Oram and D.I Perrett,...
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Tài liệu Kalman Filtering and Neural Networks P4 doc

Tài liệu Kalman Filtering and Neural Networks P4 doc

Ngày tải lên : 14/12/2013, 13:15
... in D.A Rand and L.S Young, Eds Dynamical Systems and Turbulence, Warwick 1980, Lecture Notes in Mathematics Vol 898 1981, p 230 Berlin: Springer-Verlag [6] A.M Fraser, ‘‘Information and entropy ... x2 ðk þ 1Þ ¼ 1:0 þ mfx1 ðkÞ þ x2 ðkÞ cos½mðkފg; ð4:6Þ where x1 and x2 are the real and imaginary components, respectively, of x and the parameter m is carefully chosen to be 0.7 so that the produced ... Note that A ¼ initialization and B ¼ one-step phase evaluation, the correlation dimension, Lyapunov exponents and Kolmogorov entropy of both the actual Ikeda series and the autonomously generated...
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Tài liệu Kalman Filtering and Neural Networks P5 pdf

Tài liệu Kalman Filtering and Neural Networks P5 pdf

Ngày tải lên : 14/12/2013, 13:15
... @x xk ^ D ð5:9Þ and where Rv and Rn are the covariances of vk and nk , respectively 5.2.2 EKF–Weight Estimation As proposed initially in [30], and further developed in [31] and [32], the EKF ... ð5:63Þ ^ where xkjN and pkjN are defined as the conditional mean and variance of xk ^ ^ kjN given w and all the data, fyk gN The terms xÀ and pÀ are the conditional kjN mean and variance of xÀ ... Control, 24, 36–50 (1979) [4] M Niedzwiecki and K Cisowski, ‘‘Adaptive scheme of elimination of ´ broadband noise and impulsive disturbances from AR and ARMA signals,’’ IEEE Transactions on Signal...
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Tài liệu Kalman Filtering and Neural Networks P6 pdf

Tài liệu Kalman Filtering and Neural Networks P6 pdf

Ngày tải lên : 14/12/2013, 13:15
... of f and g and the noise covariances Given observations of the (no longer hidden) states and outputs, f and g can be obtained as the solution to a possibly nonlinear regression problem, and the ... matrices A and B multiplying inputs x and u, respectively; and an output bias vector b, and the noise covariance Q Each RBF is assumed to be a Gaussian in x space, with center ci and width given ... admit exact and efficient inference (Here, and in what follows, we call a system linear if both the state evolution function and the state-to-output observation function are linear, and nonlinear...
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Tài liệu Kalman Filtering and Neural Networks P7 pptx

Tài liệu Kalman Filtering and Neural Networks P7 pptx

Ngày tải lên : 14/12/2013, 13:15
... position and velocity, and top and _ _ _ bottom pendulum angle and angular velocity, x ¼ ½x; x; y1 ; y1 ; y2 ; y2 Š The system parameters correspond to the length and mass of each pendulum, and the ... filtering (CDF) techniques developed separately by Ito and Xiong [12] and Nørgaard, Poulsen, and Ravn [13] In [7] van der Merwe and Wan show how the UKF and CDF can be unified in a general family of derivativefree ... ¼ Ck ¼  @x  @n D ^ xk ð7:29Þ  n and where Rv and Rn are the covariances of vk and nk , respectively The noise means are denoted by n ¼ E½nŠ and v ¼ E½vŠ, and are usually assumed to equal zero...
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Tài liệu Finding, Filtering, and Sorting Rows in a DataTable ppt

Tài liệu Finding, Filtering, and Sorting Rows in a DataTable ppt

Ngày tải lên : 14/12/2013, 13:15
... "server=localhost;database=Northwind;uid=sa;pwd=sa" ); SqlCommand mySqlCommand = mySqlConnection.CreateCommand(); mySqlCommand.CommandText = "SELECT TOP 10 ProductID, ProductName " + "FROM Products ... filter and sort rows, and you'll learn how to that in Chapter 13, "Using DataView Objects." Listing 11.3 shows a program that finds, filters, and sorts DataRow objects Listing 11.3: FINDFILTERANDSORTDATAROWS.CS ... OrderID and ProductID of 10248 and 11, respectively: object[] orderDetails = new object[] { 10248, 11 }; DataRow orderDetailDataRow = orderDetailsDataTable.Rows.Find(orderDetails); Filtering and...
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Tài liệu Mapping Tables and Columns docx

Tài liệu Mapping Tables and Columns docx

Ngày tải lên : 14/12/2013, 18:15
... through the TableMappings property The TableMappings property returns an object of the TableMappingCollection class This object is a collection of TableMapping objects, and you use a TableMapping object ... DataTableMapping object named myDataTableMapping, passing Customers and Cust to the Add() method: DataTableMapping myDataTableMapping = mySqlDataAdapter.TableMappings.Add("Customers", "Cust"); Notice ... You can read this mapping using the SourceTable and DataSetTable properties of myDataTableMapping For example: Console.WriteLine("myDataTableMapping.SourceTable = " + myDataTableMapping.SourceTable);...
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Tài liệu Mapping Table and Column Names Between the Data Source and DataSet docx

Tài liệu Mapping Table and Column Names Between the Data Source and DataSet docx

Ngày tải lên : 14/12/2013, 18:16
... table mapping to map the default table name 'Table' DataTableMapping dtm = da.TableMappings.Add("Table", "tblmapCategories"); // Create the column mappings for the Categories table dtm.ColumnMappings.Add("CategoryID", ... within the DataSet Each table mapping object has a collection of DataColumnMapping objects in its DataColumnMappingCollection that are accessed through its ColumnMappings property These objects ... CategoryName, and Description using the following code: dtm.ColumnMappings.Add("CategoryID", "colmapCategoryID"); dtm.ColumnMappings.Add("CategoryName", "colmapCategoryName"); dtm.ColumnMappings.Add("Description",...
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Tài liệu Kalman Filtering and Neural Networks - Chapter 4: CHAOTIC DYNAMICS pdf

Tài liệu Kalman Filtering and Neural Networks - Chapter 4: CHAOTIC DYNAMICS pdf

Ngày tải lên : 23/12/2013, 07:16
... in D.A Rand and L.S Young, Eds Dynamical Systems and Turbulence, Warwick 1980, Lecture Notes in Mathematics Vol 898 1981, p 230 Berlin: Springer-Verlag [6] A.M Fraser, ‘‘Information and entropy ... x2 ðk þ 1Þ ¼ 1:0 þ mfx1 ðkÞ þ x2 ðkÞ cos½mðkފg; ð4:6Þ where x1 and x2 are the real and imaginary components, respectively, of x and the parameter m is carefully chosen to be 0.7 so that the produced ... Note that A ¼ initialization and B ¼ one-step phase evaluation, the correlation dimension, Lyapunov exponents and Kolmogorov entropy of both the actual Ikeda series and the autonomously generated...
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Tài liệu Kalman Filtering and Neural Networks - Chapter 5: DUAL EXTENDED KALMAN FILTER METHODS docx

Tài liệu Kalman Filtering and Neural Networks - Chapter 5: DUAL EXTENDED KALMAN FILTER METHODS docx

Ngày tải lên : 23/12/2013, 07:16
... @x xk ^ D ð5:9Þ and where Rv and Rn are the covariances of vk and nk , respectively 5.2.2 EKF–Weight Estimation As proposed initially in [30], and further developed in [31] and [32], the EKF ... ð5:63Þ ^ where xkjN and pkjN are defined as the conditional mean and variance of xk ^ ^ kjN given w and all the data, fyk gN The terms xÀ and pÀ are the conditional kjN mean and variance of xÀ ... Control, 24, 36–50 (1979) [4] M Niedzwiecki and K Cisowski, ‘‘Adaptive scheme of elimination of ´ broadband noise and impulsive disturbances from AR and ARMA signals,’’ IEEE Transactions on Signal...
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Tài liệu Kalman Filtering and Neural Networks - Chapter 6: LEARNING NONLINEAR DYNAMICAL SYSTEMS USING THE EXPECTATION– MAXIMIZATION ALGORITHM doc

Tài liệu Kalman Filtering and Neural Networks - Chapter 6: LEARNING NONLINEAR DYNAMICAL SYSTEMS USING THE EXPECTATION– MAXIMIZATION ALGORITHM doc

Ngày tải lên : 23/12/2013, 07:16
... of f and g and the noise covariances Given observations of the (no longer hidden) states and outputs, f and g can be obtained as the solution to a possibly nonlinear regression problem, and the ... matrices A and B multiplying inputs x and u, respectively; and an output bias vector b, and the noise covariance Q Each RBF is assumed to be a Gaussian in x space, with center ci and width given ... admit exact and efficient inference (Here, and in what follows, we call a system linear if both the state evolution function and the state-to-output observation function are linear, and nonlinear...
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