HTK语音识别
HTK语音识别,包括在centos 7下编译通过的HTK-3.5.beta-2版本,并且改写makefile为cmake。还包括一个例子。地址;http://blog.csdn.net/philosophyatmath/article/details/64905670
HTK语音识别,包括在centos 7下编译通过的HTK-3.5.beta-2版本,并且改写makefile为cmake。还包括一个例子。地址;http://blog.csdn.net/philosophyatmath/article/details/64905670
《数理统计与数据分析(原书第3版)》内容丰富,几乎涵盖了所有经典和前沿的概率论与数理统计理论和方法,主要包括概率、随机变量、联合分布、期望、极限定理、抽样调查、参数估计、假设检验、数据汇总、两样本比较、方差分析、分类数据分析和线性最小二乘等。
在这个框架中包含base认证,日志,rest,eclipselink jpa等等。但是有个问题就是我在利用@PersistenceUnit注解是总是无法成功,也只能自己亲自完成这块代码。
Eclipse Rich Client Platform 2nd Edition with Source Code
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. The kernel methodology described here provides a powerful and unified framework for all of these disciplines, motivating algorithms that can act on general types of data (e.g. strings, vectors, text, etc.) and look for general types of relations (e.g. rankings, classifications, regressions, clusters, etc.). This book fulfils two major roles. Firstly it provides practitioners with a large toolkit of algorithms, kernels and solutions ready to be implemented, many given as Matlab code suitable for many pattern analysis tasks in fields such as bioinformatics, text analysis, and image analysis. Secondly it furnishes students and researchers with an easy introduction to the rapidly expanding field of kernel-based pattern analysis, demonstrating with examples how to handcraft an algorithm or a kernel for a new specific application, while covering the required conceptual and mathematical tools necessary to do so. The book is in three parts. The first provides the conceptual foundations of the field, both by giving an extended introductory example and by covering the main theoretical underpinnings of the approach. The second part contains a number of kernel-based algorithms, from the simplest to sophisticated systems such as kernel partial least squares, canonical correlation analysis, support vector machines, principal components analysis, etc. The final part describes a number of kernel functions, from basic examples to advanced recursive kernels, kernels derived from generative models such as HMMs and string matching kernels based on dynamic programming, as well as special kernels designed to handle text documents. All those involved in pattern recognition, machine learning, neural networks and their applications, from computational biology to text analysis will welcome this account.
We describe a new method for performing a nonlinear form of Principal Component Anal