PRINCIPLE: The UVE algorithm detects and eliminates from a PLS model (including from 1 to A components) those variables that do not carry any relevant information to model Y. The criterion used to trace the un-informative variables is the reliability of the regression coefficients: c_j=mean(b_j)/std(b_j), obtained by jackknifing. The cutoff level, below which c_j is considered to be too small, indicating that the variable j should be removed, is estimated using a matrix of random variables.The predictive power of PLS models built on the retained variables only is evaluated over all 1-a dimensions =(yielding RMSECVnew).
Inside the C++ Object Model
Inside the C++ Object Model focuses on the underlying mechanisms that support object-oriented programming within C++: constructor semantics, temporary generation, support for encapsulation, inheritance, and "the virtuals"-virtual functions and virtual inheritance. This book shows how your understanding the underlying implementation models can help you code more efficiently and with greater confidence. Lippman dispells the misinformation and myths about the overhead and complexity associated with C++, while pointing out areas in which costs and trade offs, sometimes hidden, do exist. He then explains how the various implementation models arose, points out areas in which they are likely to evolve, and why they are what they are. He covers the semantic implications of the C++ object model and how that model affects your programs.
15篇光流配準經典文獻，目錄如下：
1、A Local Approach for Robust Optical Flow Estimation under Varying
2、A New Method for Computing Optical Flow
3、Accuracy vs. Efficiency Trade-offs in Optical Flow Algorithms
4、all about direct methods
5、An Introduction to OpenCV and Optical Flow
6、Bayesian Real-time Optical Flow
7、Color Optical Flow
8、Computation of Smooth Optical Flow in a Feedback Connected Analog Network
9、Computing optical flow with physical models of brightness Variation
10、Dense estimation and object-based segmentation of the optical flow with robust techniques
11、Example Goal Standard methods Our solution Optical flow under
12、Exploiting Discontinuities in Optical Flow
13、Optical flow for Validating Medical Image Registration
14、Tutorial Computing 2D and 3D Optical Flow.pdf
15、The computation of optical flow
The library is a C++/Python implementation of the variational building block framework introduced in our papers. The framework allows easy learning of a wide variety of models using variational Bayesian learning
We address the problem of predicting a word from previous words in a sample of text. In particular,
we discuss n-gram models based on classes of words. We also discuss several statistical algorithms
for assigning words to classes based on the frequency of their co-occurrence with other words. We
find that we are able to extract classes that have the flavor of either syntactically based groupings
or semantically based groupings, depending on the nature of the underlying statistics.
state of art language modeling methods:
An Empirical Study of Smoothing Techniques for Language Modeling.pdf
BLEU, a Method for Automatic Evaluation of Machine Translation.pdf
Class-based n-gram models of natural language.pdf
Distributed Language Modeling for N-best List Re-ranking.pdf
Distributed Word Clustering for Large Scale Class-Based Language Modeling in.pdf
k-step ahead predictions determined by simulation of the
％ one-step ahead neural network predictor. For NNARMAX
％ models the residuals are set to zero when calculating the
％ predictions. The predictions are compared to the observed output.
％
This function calculates Akaike s final prediction error
％ estimate of the average generalization error for network
％ models generated by NNARX, NNOE, NNARMAX1+2, or their recursive
％ counterparts.
％
％ [FPE,deff,varest,H] = nnfpe(method,NetDef,W1,W2,U,Y,NN,trparms,skip,Chat)
％ produces the final prediction error estimate (fpe), the effective number
％ of weights in the network if it has been trained with weight decay,
％ an estimate of the noise variance, and the Gauss-Newton Hessian.
％
documentation for optimal filtering toolbox for mathematical software
package Matlab. The methods in the toolbox include Kalman filter, extended Kalman filter
and unscented Kalman filter for discrete time state space models. Also included in the toolbox
are the Rauch-Tung-Striebel and Forward-Backward smoother counter-parts for each filter, which
can be used to smooth the previous state estimates, after obtaining new measurements. The usage
and function of each method are illustrated with five demonstrations problems.
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