pendule-inverse

RSC。源码。需要的拿去。注意不同于SRC。(RSC. Source code. Need to take. Note that unlike SRC.)

论文：An examination of U.S. institutional and individual investor sentiment effect on the Turkish stock market(This study examines the effect of rational and irrational components of U.S. institutional and individual investor sentiment on Istanbul Stock Market (ISE) return and volatility. The results showthat there is a significant spillover effect of U.S. investor sentiment on stock return and volatility of ISE. A breakdown of sentiment by the type of investor shows that the impact of institutional sentiment is greater than that of individual sentiment. A breakdown of sentiment by rationality shows that the effect of rational sentiment on ISE return is faster though not necessarily greater than that of irrational sentiment. The conclusion fromthese results is that the effect of U.S. investor sentiment is systemic and cannot be diversified away. U.S. investor sentiment, therefore, constitutes a priced risk factor and must be accounted for accordingly in international asset pricing models. The findings also provide some evidence of a negative relationship between U.S. investor senti)

Fortran和Matlab混合编程简介(Fortran and Matlab Introduction)

MATLAB实现ELM算法，并附带UCI经典数据。(MATLAB realization ELM algorithm, along with the classic UCI data.)

ISODATA法分类IRIS数据集，压缩包中包括两个.m文件，运行provaisodata_4D.m可直接分类。或者通过isodata_4D.m设置参数也可以得到结果。程序有详细注释，很容易懂。最后结果既有文字描述，也有图形输出。(ISODATA classification method IRIS data set, compressed package includes two. M files, run provaisodata_4D.m direct classification. Or set the parameters by isodata_4D.m can get results. Procedures detailed notes, it is easy to understand. Final results of both the written description, there are graphical output.)

decomposition of .wavfile

This is matlab model for ASK modulation techniques.

Eigenvalues and their algebraic multiplicity.

Hidden-Unit Conditional Random Fields 工具箱，可以用于训练linearCRF和和L.J.P. van der Maaten, M. Welling 提出的huCRF(We provide Matlab code that implements the training and evaluation of hidden-unit CRFs, as well as code to reproduce the results of our experiments. The code implements four different training algorithms: (1) a batch learner that uses L-BFGS, (2) a stochastic gradient descent learner, (3) an online perceptron training algorithm, and (4) an online large-margin perceptron algorithm. The code can also be used to perform (conditional) herding in hidden-unit CRFs. )

CSS documents for beeginner