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Statistical-Learning-theory

  • 三本經(jīng)典UML 2.0書(shū)籍

    三本經(jīng)典UML 2.0書(shū)籍, OReilly.Learning.UML.2.0.Apr.2006.chm OReilly.UML.2.0.in.a.Nutshell.Jun.2005.chm UML Distilled_3Ed.chm

    標(biāo)簽: UML 2.0 書(shū)籍

    上傳時(shí)間: 2015-08-19

    上傳用戶:dapangxie

  • * * THIS SOFTWARE IS PROVIDED BY THE AUTHOR ``AS IS AND ANY EXPRESS * OR IMPLIED WARRANTIES, INC

    * * THIS SOFTWARE IS PROVIDED BY THE AUTHOR ``AS IS AND ANY EXPRESS * OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED * WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE * ARE DISCLAIMED. IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY * DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL * DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE * GOODS OR SERVICES LOSS OF USE, DATA, OR PROFITS OR BUSINESS * INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, * WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING * NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS * SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH D

    標(biāo)簽: WARRANTIES SOFTWARE PROVIDED EXPRESS

    上傳時(shí)間: 2013-12-20

    上傳用戶:sxdtlqqjl

  • by Randal L. Schwartz and Tom Phoenix ISBN 0-596-00132-0 Third Edition, published July 2001. (See

    by Randal L. Schwartz and Tom Phoenix ISBN 0-596-00132-0 Third Edition, published July 2001. (See the catalog page for this book.) the text of Learning Perl, 3rd Edition. Table of Contents Copyright Page Preface Chapter 1: Introduction Chapter 2: Scalar Data Chapter 3: Lists and Arrays Chapter 4: Subroutines Chapter 5: Hashes Chapter 6: I/O Basics Chapter 7: Concepts of Regular Expressions Chapter 8: More About Regular Expressions Chapter 9: Using Regular Expressions Chapter 10: More Control Structures Chapter 11: Filehandles and File Tests Chapter 12: Directory Operations Chapter 13: Manipulating Files and Directories Chapter 14: Process Management Chapter 15: Strings and Sorting Chapter 16: Simple Databases Chapter 17: Some Advanced Perl Techniques Appendix A: Exercise Answers Appendix B: Beyond the Llama Index Colophon

    標(biāo)簽: L. published Schwartz Edition

    上傳時(shí)間: 2014-11-29

    上傳用戶:kr770906

  • by Randal L. Schwartz and Tom Phoenix ISBN 0-596-00132-0 Third Edition, published July 2001. (See

    by Randal L. Schwartz and Tom Phoenix ISBN 0-596-00132-0 Third Edition, published July 2001. (See the catalog page for this book.) Learning Perl, 3rd Edition. Table of Contents Copyright Page Preface Chapter 1: Introduction Chapter 2: Scalar Data Chapter 3: Lists and Arrays Chapter 4: Subroutines Chapter 5: Hashes Chapter 6: I/O Basics Chapter 7: Concepts of Regular Expressions Chapter 8: More About Regular Expressions Chapter 9: Using Regular Expressions Chapter 10: More Control Structures Chapter 11: Filehandles and File Tests Chapter 12: Directory Operations Chapter 13: Manipulating Files and Directories Chapter 14: Process Management Chapter 15: Strings and Sorting Chapter 16: Simple Databases Chapter 17: Some Advanced Perl Techniques Appendix A: Exercise Answers Appendix B: Beyond the Llama Index Colophon

    標(biāo)簽: L. published Schwartz Edition

    上傳時(shí)間: 2015-09-03

    上傳用戶:lifangyuan12

  • 一個(gè)由Mike Gashler完成的機(jī)器學(xué)習(xí)方面的includes neural net, naive bayesian classifier, decision tree, KNN, a genet

    一個(gè)由Mike Gashler完成的機(jī)器學(xué)習(xí)方面的includes neural net, naive bayesian classifier, decision tree, KNN, a genetic algorithm, and some manifold learning algorithms.

    標(biāo)簽: classifier includes bayesian decision

    上傳時(shí)間: 2015-09-11

    上傳用戶:gaome

  • a Java toolkit for training, testing, and applying Bayesian Network Classifiers. Implemented classif

    a Java toolkit for training, testing, and applying Bayesian Network Classifiers. Implemented classifiers have been shown to perform well in a variety of artificial intelligence, machine learning, and data mining applications.

    標(biāo)簽: Classifiers Implemented Bayesian applying

    上傳時(shí)間: 2015-09-11

    上傳用戶:ommshaggar

  • Linux driver for FujiFilm FinePix digital cameras in PC-CAM (i.e. webcam) mode driver/ Conta

    Linux driver for FujiFilm FinePix digital cameras in PC-CAM (i.e. webcam) mode driver/ Contains the driver sources. Compile with "make". Then as root, install with "make install". Type "modprobe finepix" to load the module. userspace/ Type "make" to compile. fpix: test program, independant of the driver. Uses libusb to directly access the camera and capture a frame, saved under frame.jpg. Will not work if the driver is loaded. fpixtest: test program. Uses V4L2 to capture an image. fpix-stress-v4l2: never ending (in theory) stress test derived from fpixtest.

    標(biāo)簽: driver i.e. FujiFilm FinePix

    上傳時(shí)間: 2014-11-28

    上傳用戶:chenxichenyue

  • The MatlabBGL library fills a hole in Matlab s suite of algorithms. Namely, it provides a rich set o

    The MatlabBGL library fills a hole in Matlab s suite of algorithms. Namely, it provides a rich set of algorithms to work with graphs, as in graph theory graphs. The MatlabBGL package uses Matlab s native sparse matrix type as a graph and provides algorithms that work

    標(biāo)簽: algorithms MatlabBGL provides library

    上傳時(shí)間: 2014-11-17

    上傳用戶:zyt

  • Robustnesstochangesinilluminationconditionsaswellas viewing perspectives is an important requirement

    Robustnesstochangesinilluminationconditionsaswellas viewing perspectives is an important requirement formany computer vision applications. One of the key fac-ors in enhancing the robustness of dynamic scene analy-sis that of accurate and reliable means for shadow de-ection. Shadowdetectioniscriticalforcorrectobjectde-ection in image sequences. Many algorithms have beenproposed in the literature that deal with shadows. How-ever,acomparativeevaluationoftheexistingapproachesisstill lacking. In this paper, the full range of problems un-derlyingtheshadowdetectionareidenti?edanddiscussed.Weclassifytheproposedsolutionstothisproblemusingaaxonomyoffourmainclasses, calleddeterministicmodeland non-model based and statistical parametric and non-parametric. Novelquantitative(detectionanddiscrimina-ionaccuracy)andqualitativemetrics(sceneandobjectin-dependence,?exibilitytoshadowsituationsandrobustnesso noise) are proposed to evaluate these classes of algo-rithms on a benchmark suite of indoor and outdoor videosequences.

    標(biāo)簽: Robustnesstochangesinillumination conditionsaswellas perspectives requirement

    上傳時(shí)間: 2014-01-23

    上傳用戶:whenfly

  • The Algorithm of Detectors Generating in Intrusion Detection System Based on Artificial Immune Theo

    The Algorithm of Detectors Generating in Intrusion Detection System Based on Artificial Immune Theory

    標(biāo)簽: Artificial Generating Algorithm Detectors

    上傳時(shí)間: 2013-12-27

    上傳用戶:陽(yáng)光少年2016

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