下载 >  课程资源 >  专业指导 > Pattern Recognition With Neural Networks In C.pdf
5

Pattern Recognition With Neural Networks In C.pdf

Why do we feel a need to write a book about pattern recognition when many excellent books are already available on this classical topic? The answer lies in the depth of our coverage of neural networks as natural pattern classifiers and clusterers. Artificial neural network computing has emerged as an extremely active research area with a central focus on manipulation of pattern-formatted information, information containing an underlying pattern. This has given rise to a new coherent approach to pattern recognition which builds u pon both the contributions of the past and the rapid progress in neural network research. Pattern recognition has grown to encompass a wider scope of methodology than is available in the traditional domain of statistical pattern recognition. The addition of artificial neural network computing to traditional pattern recognition has given rise to a new, different, and more powerful methodology which we intended to present in this book for the practitioner. ...展开详情收缩
2011-11-15 上传大小:14.89MB
想读
分享
收藏 举报
机器学习,深度学习, Neural_Networks_for_Pattern_Recognition_-_Christopher_Bishop

最经典的机器学习,深度学习,材料,目前国内外下载量和引用率最高Neural_Networks_for_Pattern_Recognition_-_Christopher_Bishop

立即下载
Pattern Recognition and Neural Networks (B.D.Ripley).pdf

edit by ripley. university oxford

立即下载
Neural Networks for Pattern Recognition

Neural Networks for Pattern RecognitionNeural Networks for Pattern RecognitionNeural Networks for Pattern RecognitionNeural Networks for Pattern Recognition

立即下载
Pattern Recognition with Neural Networks in C++(C++在模式识别与神经网络应用)

分章节给出了Pattern Recognition with Neural Networks in C++(C++在模式识别与神经网络应用)的代码,对于初学者有较好的借鉴作用

立即下载
Pattern Recognition with neural networks in C++ (配套源码光盘)

这是书籍《神经网络模式识别及其实现-Pattern Recognition with neural networks in C++》,(美)Abhijit S. Pandya,Robert B. Macy著,徐勇,荆涛等译,的配套光盘,里面有书籍里面的C++代码,可以参考。我感觉书里的C++代码在IBM的os/2操作系统上实现,Windows系统下编译会有错误。不过里面的代码仍然值得参考。

立即下载
Bishop Pattern Recognition

模式识别领域最经典的一本算法书,748页完整版。

立即下载
Pattern recognition with neural networks in C++

Pattern recognition with neural networks in C++

立即下载
Speech recognition with deep recurrent neural networks论文翻译

循环神经网络在语音识别中的应用 LSTM 双向RNN 双向lstm

立即下载
DeepID3_Face_Recognition_with_Very_Deep_Neural_Networks

DeepID3_Face_Recognition_with_Very_Deep_Neural_Networks.pdf

立即下载
DeepID3: Face Recognition with Very Deep Neural Networks英文论文翻译

人脸识别论文,手动翻译,花费两天时间希望可以帮助到大家 DeepID3: Face Recognition with Very Deep Neural Networks Yi Sun1 Ding Liang2 Xiaogang Wang3,4 Xiaoou Tang1,4

立即下载
Pattern Recognition with Neural Networks in C++.pdf

太辛苦了!!这个pdf是我从chm格式原文档重新排版并用adobe acrobat pro7.0 转化过来的!并且包含书签,非常便于阅读和注释!! 注:chm格式的和本书的所谓的随书光盘csdn上都可以下载!

立即下载
Pattern Recognition with Neural Networks in C++

Preface Acknowledgment Chapter 1—Introduction 1.1 Pattern Recognition Systems 1.2 Motivation For Artificial Neural Network Approach 1.3 A Prelude To Pattern Recognition 1.4 Statistical Pattern Recognition 1.5 Syntactic Pattern Recognition 1.6 The Character Recognition Problem 1.7 Organization Of Topics References And Bibliography Chapter 2—Neural Networks: An Overview 2.1 Motivation for Overviewing Biological Neural Networks 2.2 Background 2.3 Biological Neural Networks 2.4 Hierarchical Organization in the Brain 2.5 Historical Background 2.6 Artificial Neural Networks References and Bibliography Chapter 3—Preprocessing 3.1 General 3.2 Dealing with Input from a Scanned Image 3.3 Image Compression 3.3.1 Image Compression Example 3.4 Edge Detection 3.5 Skeletonizing 3.5.1 Thinning Example 3.6 Dealing with Input From a Tablet 3.7 Segmentation References and Bibliography Chapter 4—Feed-Forward Networks with Supervised Learning 4.1 Feed-Forward Multilayer Perceptron (FFMLP) Architecture 4.2 FFMLP in C++ 4.3 Training with Back Propagation 4.3.1 Back Propagation in C++ 4.4 A Primitive Example 4.5 Training Strategies and Avoiding Local Minima 4.6 Variations on Gradient Descent 4.6.1 Block Adaptive vs. Data Adaptive Gradient Descent 4.6.2 First-Order vs. Second-Order Gradient Descent 4.7 Topology 4.8 ACON vs. OCON 4.9 Overtraining and Generalization 4.10 Training Set Size and Network Size 4.11 Conjugate Gradient Method 4.12 ALOPEX References and Bibliography Chapter 5—Some Other Types of Neural Networks 5.1 General 5.2 Radial Basis Function Networks 5.2.1 Network Architecture 5.2.2 RBF Training 5.2.3 Applications of RBF Networks 5.3 Higher Order Neural Networks 5.3.1 Introduction 5.3.2 Architecture 5.3.3 Invariance to Geometric Transformations 5.3.4 An Example 5.3.5 Practical Applications References and Bibliography Chapter 6—Feature Extraction I: Geometric Features and Transformations 6.1 General 6.2 Geometric Features (Loops, Intersections, and Endpoints) 6.2.1 Intersections and Endpoints 6.2.2 Loops 6.3 Feature Maps 6.4 A Network Example Using Geometric Features 6.5 Feature Extraction Using Transformations 6.6 Fourier Descriptors 6.7 Gabor Transformations and Wavelets References And Bibliography Chapter 7—Feature Extraction II: Principal Component Analysis 7.1 Dimensionality Reduction 7.2 Principal Components 7.2.1 PCA Example 7.3 Karhunen-Loeve (K-L) Transformation 7.3.1 K-L Transformation Example 7.4 Principal Component Neural Networks 7.5 Applications References and Bibliography Chapter 8—Kohonen Networks and Learning Vector Quantization 8.1 General 8.2 The K-Means Algorithm 8.2.1 K-Means Example 8.3 An Introduction To The Kohonen Model 8.3.1 Kohonen Example 8.4 The Role Of Lateral Feedback 8.5 Kohonen Self-Organizing Feature Map 8.5.1 SOFM Example 8.6 Learning Vector Quantization 8.6.1 LVQ Example 8.7 Variations On LVQ 8.7.1 LVQ2 8.7.2 LVQ2.1 8.7.3 LVQ3 8.7.4 A Final Variation Of LVQ References And Bibliography Chapter 9—Neural Associative Memories and Hopfield Networks 9.1 General 9.2 Linear Associative Memory (LAM) 9.2.1 An Autoassociative LAM Example 9.3 Hopfield Networks 9.4 A Hopfield Example 9.5 Discussion 9.6 Bit Map Example 9.7 Bam Networks 9.8 A Bam Example References And Bibliography Chapter 10—Adaptive Resonance Theory (ART) 10.1 General 10.2 Discovering The Cluster Structure 10.3 Vector Quantization 10.3.1 VQ Example 1 10.3.2 VQ Example 2 10.3.3 VQ Example 3 10.4 Art Philosophy 10.5 The Stability-Plasticity Dilemma 10.6 ART1: Basic Operation 10.7 ART1: Algorithm 10.8 The Gain Control Mechanism 10.8.1 Gain Ccontrol Example 1 10.8.2 Gain Control Example 2 10.9 ART2 Model 10.10 Discussion 10.11 Applications References and Bibliography Chapter 11—Neocognitron 11.1 Introduction 11.2 Architecture 11.3 Example of a System with Sample Training Patterns References and Bibliography Chapter 12—Systems with Multiple Classifiers 12.1 General 12.2 A Framework for Combining Multiple Recognizers 12.3 Voting Schemes 12.4 The Confusion Matrix 12.5 Reliability 12.6 Some Empirical Approaches References and Bibliography Index

立即下载
斯坦福大学公开课 CS231n_Convolutional_Neural_Networks_for_Visual_Recognition PPT

深度学习-面向视觉识别的卷积神经网络,2016斯坦福大学公开课。课程介绍: 计算机视觉在社会中已经逐渐普及,并广泛运用于搜索检索、图像理解、手机应用、地图导航、医疗制药、无人机和无人驾驶汽车等领域。而这些应用的核心技术就是图像分类、图像定位和图像探测等视觉识别任务。近期神经网络(也就是“深度学习”)方法上的进展极大地提升了这些代表当前发展水平的视觉识别系统的性能。 本课程将深入讲解深度学习框架的细节问题,聚焦面向视觉识别任务(尤其是图像分类任务)的端到端学习模型。在10周的课程中,学生们将会学习如何实现、训练和调试他们自己的神经网络,并建立起对计算机视觉领域的前沿研究方向的细节理解。最终的作业将包括训练一个有几百万参数的卷积神经网络,并将其应用到最大的图像分类数据库(ImageNet)上。我们将会聚焦于教授如何确定图像识别问题,学习算法(比如反向传播算法),对网络的训练和精细调整(fine-tuning)中的工程实践技巧,指导学生动手完成课程作业和最终的课程项目。本课程的大部分背景知识和素材都来源于ImageNet Challenge竞赛。 主讲人: 李飞飞,斯坦福大学计算机科学系副教授。担任斯坦福大学人工智能实验室和视觉实验室主任,主要研究方向为机器学习、计算机视觉、认知计算神经学。她在TED上的演讲,如何教计算机理解图片。

立即下载
模式识别书籍汇总 英文原版

包括Pattern Recognition and Machine Learning、Feature Extraction and Image Processing、Neural Networks:A Comprehensive Foundation.3rd、Pattern Recognition.3rd、Statistical pattern recognition、Pattern Classification 以及 Wavelet Tutorial

立即下载
Facial Expression Recognition with Convolutional Neural Networks

Facial Expression Recognition with Convolutional Neural Networks

立即下载
Pattern Recognition With Neural Networks In C++(source)

Pattern Recognition With Neural Networks In C++, (Book disk) by Abhijit Pandya, IEEE Press 1995

立即下载
Pattern Recognition Using Neural and Functional Networks

ISBN: 9783540851295 - 3540851291 Paperback: 150 pages Data: December 1, 2008 新书啊!!!! Description: Biologically inspired computing is different from conventional computing. It has a different feel; often the terminology does not sound like it’s talking about machines. The activities of this computing sound more human than mechanistic as people speak of machines that behave, react, self-organize, learn, generalize, remember and even to forget. Much of this technology tries to mimic nature’s approach in order to mimic some of nature’s capabilities. They have a rigorous, mathematical basis and neural networks for example have a statistically valid set on which the network is trained. Two outlines are suggested as the possible tracks for pattern recognition. They are neural networks and functional networks. Neural Networks (many interconnected elements operating in parallel) carry out tasks that are not only beyond the scope of conventional processing but also cannot be understood in the same terms. Imaging applications for neural networks seem to be a natural fit. Neural networks love to do pattern recognition. A new approach to pattern recognition using microARTMAP together with wavelet transforms in the context of hand written characters, gestures and signatures have been dealt. The Kohonen Network, Back Propagation Networks and Competitive Hopfield Neural Network have been considered for various applications. Functional networks, being a generalized form of Neural Networks where functions are learned rather than weights is compared with Multiple Regression Analysis for some applications and the results are seen to be coincident. New kinds of intelligence can be added to machines, and we will have the possibility of learning more about learning. Thus our imaginations and options are being stretched. These new machines will be fault-tolerant, intelligent and self-programming thus trying to make the machines smarter. So as to make those who use the techniques even smarter.

立即下载
模式分析的核方法英文版

Pattern Analysis is the process of finding general relations in a set of data, and forms the core of many disciplines, from neural networks to so-called syntactical pattern recognition, from statistical pattern recognition to machine learning and data mining. Applications of pattern analysis range from bioinformatics to document retrieval.

立即下载
Neural Networks for Face Recognition

This Code gives you an opportunity to apply neural network learning to the problem of face recognition. The program comes from Carnegie Mellon University. Interested parties are welcome to discuss and I would like to talk. mail:xyz529@hotmail.com

立即下载
卷积神经网络在字符识别中的应用

Character Recognition Using Convolutional Neural Networks

立即下载
关闭
img

spring mvc+mybatis+mysql+maven+bootstrap 整合实现增删查改简单实例.zip

资源所需积分/C币 当前拥有积分 当前拥有C币
5 0 0
点击完成任务获取下载码
输入下载码
为了良好体验,不建议使用迅雷下载
img

Pattern Recognition With Neural Networks In C.pdf

会员到期时间: 剩余下载个数: 剩余C币: 剩余积分:0
为了良好体验,不建议使用迅雷下载
VIP下载
您今日下载次数已达上限(为了良好下载体验及使用,每位用户24小时之内最多可下载20个资源)

积分不足!

资源所需积分/C币 当前拥有积分
您可以选择
开通VIP
4000万
程序员的必选
600万
绿色安全资源
现在开通
立省522元
或者
购买C币兑换积分 C币抽奖
img

资源所需积分/C币 当前拥有积分 当前拥有C币
5 4 45
为了良好体验,不建议使用迅雷下载
确认下载
img

资源所需积分/C币 当前拥有积分 当前拥有C币
5 0 0
为了良好体验,不建议使用迅雷下载
VIP和C币套餐优惠
img

资源所需积分/C币 当前拥有积分 当前拥有C币
5 4 45
您的积分不足,将扣除 10 C币
为了良好体验,不建议使用迅雷下载
确认下载
下载
您还未下载过该资源
无法举报自己的资源

兑换成功

你当前的下载分为234开始下载资源
你还不是VIP会员
开通VIP会员权限,免积分下载
立即开通

你下载资源过于频繁,请输入验证码

您因违反CSDN下载频道规则而被锁定帐户,如有疑问,请联络:webmaster@csdn.net!

举报

若举报审核通过,可返还被扣除的积分

  • 举报人:
  • 被举报人:
  • *类型:
    • *投诉人姓名:
    • *投诉人联系方式:
    • *版权证明:
  • *详细原因: