internet and web technology lecture notes
Data Mining Classification: Basic Concepts, Decision Trees, and Model Evaluation Lecture Notes for Chapter 4 Introduction to Data Mining pptx
... it–Class counts in each of the partitions, A < v and A ≥ vSimple method to choose best v–For each v, scan the database to gather count matrix and compute its Gini index–Computationally Inefficient! ... or random coilCategorizing news stories as finance, weather, entertainment, sports, etc© Tan,Steinbach, Kumar Introduction to Data Mining 47 Stopping Criteria for Tree InductionStop expanding ... attribute,–Sort the attribute on values–Linearly scan these values, each time updating the count matrix and computing gini index–Choose the split position that has the least gini indexCheat No No...
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Getting Started With ASP.NET ASP.NET is a new and powerful technology for writing dynamic web pages.
... web server machine. Browsing to a Page on your Web Server Now you know the name of your web server, and that web services are running; you can view some classic ASP pages hosted on your web ... dynamic web pages. How are Dynamic Web Pages Served? To fully understand the nature of dynamic web pages, we first need to look at the limitations of what we can and can't do with a static web ... on the Internet. The URL is an http:// web page address which indicates which web server to connect to, and the page we want to view. What URL do we use in order to browse to our web server?...
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Tài liệu Lecture Notes in Control and Information SciencesEditors: M. Thoma pdf
... in du stri es, and expect simultaneously that the researchplantedthe root in this kind of ground will be expanded at the researchinstitute etc.of an enterpriseand, expecially and university.At ... (2.40) is called ajointlinearized model.Here, u1( t )and u2( t )denotes the angle input of axis 1and axis2,respec-tively. Kpdenotes Kp 1ofequation(2.23)inthelowspeed1stordermodelof2.2.3.Fig.2.12illustratestheblockdiagramofthe1stordersystem.Inthissection, ... commandtoservosystem is aproblem. Thisproblemisabout the form of time functionofcommand.The problemofcommandcontaining the way of data given mustbe discussed.In the discussion of this commandsystem,...
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Tài liệu Lecture Notes in Geoinformation and Cartography pdf
... Region Segregation and Query Real Time Alarm, Display and Process-ing Internet Information Publishing Epidemic Information Publishing on Web Statistical Query on Web Decision Analysis ... National Science Foundation under Grants #40471111 and #70571076, and by the 973 Project under Grant #2001CB5103. Lecture Notes in Geoinformation and Cartography Series Editors: William Cartwright, ... (5) where Y denotes the response variable; X the causal variable; and f a sta-tistical function between Y and X. f could be a spatial linear regression function such as SAR, MA and CAR (Anselin...
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Tài liệu Lecture Notes in Economics and Mathematical Systems pdf
... tragicfate and the mathematical legacy of W. D¨oblin see Bru and Yor (2002).IntroductionThe lecture notes are organized as follows: Chapter 1 gives a conciseoverview of the theory of Lebesgue and ... differentiation and integration can be stated asXt= X0+t0˙Xsds2.6 Stopping Times and Local Martingales 41Proof. From Lemma 2.6.6 take Tn↓ T and d ∈ Dn.1) For XTn∈FTn and B ∈Bd, ... XT(ω):=XT (ω)(ω)is FT-measurable. Lecture Notes in Economics and Mathematical Systems 579Founding Editors:M. BeckmannH.P. KünziManaging Editors:Prof. Dr. G. FandelFachbereich WirtschaftswissenschaftenFernuniversität...
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Lecture Notesin Control and Information Sciences ppt
... where p~, Apt, Pl and P2 are position/orientation vectors corresponding to s~, st, Sl and s2, respectively. The positions/orientations p,, Pl and P2 are those of S~, $1 and $2 in Figure 1.2, ... The velocity and force ellip- soids, and extension of grasp stability and manipulability are presented in Section 2.3. Section 2.4 presents a number of examples. Terminology and Notation: ... - s2 (1.18) where s~, Asr, Sl and s2 are velocity vectors corresponding to f~, f~, fl and f2, respectively. The velocities s~, sl and s2 are those of Z~, ~1 and E~ in Figure 1.2, respectively....
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Lecture Notes in Control and Information Sciences Editors: M. Thoma · M. Morari316.R.V. Patel pptx
... underwater, and hazardous material handling have led toconsiderable activity in the following research areas:• Contact Force Control (CFC) and compliant motion control• Redundant manipulators and ... Jacobian matrix, and being the and Jacobian matrices of the main and additional tasks respectively.The velocity kinematics of the extended task are given by:(2.3.9)where and are the time ... function:(2.3.20)where , and are diagonal positive-defi-nite weighting matrices that assign priority between the main, additional, and singularity robustness tasks. and are then- and k -dimensional...
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Data Mining Association Analysis: Basic Concepts and Algorithms Lecture Notes for Chapter 6 Introduction to Data Mining pdf
... each candidate itemset–To reduce the number of comparisons, store the candidates in a hash structure• Instead of matching each transaction against every candidate, match it against candidates ... Lattice–General-to-specific vs Specific-to-generalData Mining Association Analysis: Basic Concepts and Algorithms Lecture Notes for Chapter 6Introduction to Data MiningbyTan, Steinbach, Kumar© Tan,Steinbach, ... confidence• Thus, we may decouple the support and confidence requirements© Tan,Steinbach, Kumar Introduction to Data Mining 16 Reducing Number of ComparisonsCandidate counting:–Scan the database...
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Data Mining Association Rules: Advanced Concepts and Algorithms Lecture Notes for Chapter 7 Introduction to Data Mining docx
... found–Candidate Generation: •Merge pairs of frequent subsequences found in the (k-1)th pass to generate candidate sequences that contain k items –Candidate Pruning:•Prune candidate k-sequences ... RulesHow do support and confidence vary as we traverse the concept hierarchy?–If X is the parent item for both X1 and X2, then σ(X) ≤ σ(X1) + σ(X2)–If σ(X1 ∪ Y1) ≥ minsup, and X is parent ... new pass over the sequence database D to find the support for these candidate sequences–Candidate Elimination:•Eliminate candidate k-sequences whose actual support is less than minsup© Tan,Steinbach,...
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Data Mining Cluster Analysis: Basic Concepts and Algorithms Lecture Notes for Chapter 8 Introduction to Data Mining pot
... clusters are irregular or intertwined, and when noise and outliers are present. 6 density-based clusters© Tan,Steinbach, Kumar Introduction to Data Mining 34 Handling Empty ClustersBasic K-means ... the distance to the nearest cluster–To get SSE, we square these errors and sum them.–x is a data point in cluster Ci and mi is the representative point for cluster Ci • can show that ... problem to a different domain and solve a related problem in that domain–Proximity matrix defines a weighted graph, where the nodes are the points being clustered, and the weighted edges represent...
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Data Mining Cluster Analysis: Advanced Concepts and Algorithms Lecture Notes for Chapter 9 Introduction to Data Mining pot
... points share more than T neighbors and –the two points are in each others k nearest neighbor listFor instance, we might choose a nearest neighbor list of size 20 and put points in the same cluster ... Can Handle Differing DensitiesOriginal PointsSNN Clustering© Tan,Steinbach, Kumar Introduction to Data Mining 13 Limitations of Current Merging SchemesCloseness schemes will merge (a) and ... reduced–The size of the problems that can be handled is increased © Tan,Steinbach, Kumar Introduction to Data Mining 35 SNN Clustering Can Handle Other Difficult Situations© Tan,Steinbach,...
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MBA 604 Introduction Probaility and Statistics Lecture Notes potx
... 891 Introduction 892 OneWayANOVA:CompletelyRandomizedExperimentalDesign 903 TheRandomizedBlockDesign 933Brand 1 Brand 2Brand 1 90/100 10/100Brand 2 40/200 160/200SoP =0.90.10.20.8Question ... 2(ii)x-2146p(x).2.2.2.143Chapter 3Random Variables and DiscreteDistributionsContents.Random VariablesExpected Values and VarianceBinomialPoissonHypergeometric1 Random VariablesThe discrete ... (A)=.2, and P (B)=.4, are A and B mutually exclusive?independent?(ii) If P (A ∪B)=.65, P (A)=.3, and P (B)=.5, are A and B mutually exclusive?independent?(iii) If P (A ∪ B)=.7, P (A)=.4, and...
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Lecture Notes in Control and Information SciencesEditor: M. Thoma229.J.-P.Laumond (Ed.) doc
... York British Library Cataloguing in Publication Data Robot motion planning and control. - (Lecture notes in control and information sciences ; 229) 1.Robots - Motion 2.Robots - Control systems ... Congress Cataloging-in-Publication Data Robot motion planning and control. / J. -P. Laumond (ed.). p. crr~ - - (Lecture notes in control and information sciences ; 229) Includes bibliographical ... allowing to compute 62 (resp. 81 and 8) from (i2,~/2) (resp. (~h,yh) and (J/2,~/2)). Finally the controls v and w are given by v = cos0 A_ =~. (or V = sin 8 ) and w Therefore any variable...
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Modeling the Internet and the Web pdf
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