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Feedforward network, for example, we look at the typical two-layer network of Figure 5.1, and examine a hidden-layer element, if we take the symbol of its input parameter all inverse, take the tanh function as an example, we will get the opposite excitation function value, namely Tanh (−a) =−tanh (a). And then the unit all the output connection weights are reversed, we can get the same output, that is to say, there are two different sets of weights can be obtained the same output value. If ther

] = \displaystyle{\sum_{m=0}}mbin (m| N,\MU) =n\mu\)\ (Var[m] = \displaystyle{\sum_{m=0}} (M-\mathbb{e}[m]) ^{2}bin (m| N,\MU) =n\mu (1-\MU) \)
Beta distribution (distribution)
This section considers how to introduce a priori information into a binary distribution and introduce a conjugate priori (conjugacy prior)Beta distribution is introduced as a priori probability distribution, which is controlled by two hyper-parameters \ (A, b\).
\ (Beta (\mu|a,b) =\frac{\gamma

, the minimum value of the price function jval provided by us, of course, returns the solution of the vector θ.
The above method is obviously applicable to regular logistic regression.5. Conclusion
Through several recent articles, we can easily find that both linear regression and logistic regression can be solved by constructing polynomials. However, you will gradually find that more powerful non-linear classifiers can be used to solve polynomial regression problems. In the next article, we wil

Fortunately with the last two months of spare time to "statistical machine learning" a book a rough study, while combining the "pattern recognition", "Data mining concepts and technology" knowledge point, the machine learning of s

(written in front) said yesterday to write a machine learning book, then write one today. This book is mainly used for beginners, very basic, suitable for sophomore, junior to see the children, of course, if you are a senior or a senior senior not seen machine learning is also applicable. Whether it's studying intellig

Reprinted please indicate Source Address: http://www.cnblogs.com/xbinworld/archive/2013/04/21/3034300.html
Pattern Recognition and machine learning (PRML) book learning, Chapter 1.1, introduces polynomial curve fitting)
The doctor is almost finished. He will graduate

Original writing. For more information, see http://blog.csdn.net/xbinworld,bincolumns.
Pattern Recognition and machine learning (PRML) book learning, Chapter 1.1, introduces polynomial curve fitting)
The doctor is almost finished. He will graduate next year and start prepari

Original writing. For reprint, please indicate that this article is from:Http://blog.csdn.net/xbinworld, Bin Column
Pattern Recognition and machine learning (PRML), Chapter 1.2, probability theory (I)
This section describes the essence of probability theory in the entire book, highlighting an uncertainty understand

Original writing, reproduced please indicate the source of http://www.cnblogs.com/xbinworld/archive/2013/04/25/3041505.html
Today I will start learning pattern recognition and machine learning (PRML), Chapter 1.2, probability theory (I)
This section describes the e

Bishop's masterpiece "Pattern recognitionand machine learning" has long been stationed in my hard drive for more than a year, Zennai fear of its vast number of pages, has not dared to start. Recently read the literature, repeatedly quoted. Had to turn it over and prepare to read it carefully. If you have the conditions, you should also write a reading note, or ba

ObjectiveSince machine learning is generated from computer science, image recognition originates from engineering. However, these activities can be seen as two aspects of the same field, and they have undergone a fundamental development in the past 10 years. In particular, when the image model has emerged as a framework for describing and applying probabilistic m

and do not add more categorical information are removed.Description In fact, the task of feature selection and extraction should be carried out before the design of the classifier, and it is more helpful to understand the problem by describing the feature selection and extraction after discussing the classifier design from the common pattern recognition teaching experience.Feature Selection: It is from t

Pattern recognition originated in engineering, and machine learning originated in computer science. However, these different disciplines can be seen as a different direction in a field and have experienced considerable development over the last few decades. It is particularly pointed out that the Bayesian method (Bayes

, and the use of GK as a sub-standard is inappropriate. Therefore, if the class probability density function is not or is not approximate to the normal distribution, the mean and variance are not sufficient to estimate the classification of categories, at which point the criterion function is not fully applicable.The greater the dispersion between the class and the Inter-class dispersion matrix SW and the SB class, the smaller the dispersion in the class, the better the scalability. Scatter matr

the above accuracy problems:But the calculation is almost twice times the amount of (5.68). In fact, the calculation of numerical methods can not take advantage of the previous useful information, each derivative needs to be calculated independently, the calculation can not be simplified.But the interesting thing is that the numerical derivative is useful in another place--gradient check! We can use the results of the central differences and the derivative of the BP algorithm to compare, in ord

). In fact, the calculation of numerical methods can not take advantage of the previous useful information, each derivative needs to be calculated independently, the calculation can not be simplified.But the interesting thing is that the numerical derivative is useful in another place--gradient check! We can use the results of the central differences and the derivative of the BP algorithm to compare, in order to determine whether the BP algorithm execution is correct.Starting today to learn the

Tags: tin mac reg ATI Learning-Bayesian att complexity testIn fact, it only took a little time to study the book today,If the model has too many parameters, and the training data is not enough, there will be overfitting.Overfitting can be solved by regularization, the Bayesian method can also avoid the appearance of overfitting, in fact, in the Bayesian model, the effective parameters of the model is automatically determined by the size of the trainin

to the derivative of the scalar y-to-column vector x,The y is biased for the elements of each x without transpose.DY/DX = [Dy/dx (IJ)]Important Conclusions:y = U ' XV =σσu (i) x (IJ) v (j) then Dy/dx = = UV 'y = U ' X ' XU then dy/dx = 2XUU 'y = (xu-v) ' (xu-v) then dy/dx = d (U ' X ' xu-2v ' XU + V ' V)/dx = 2XUU '-2VU ' + 0 = 2 (xu-v) U '9. Derivative of matrix Y to matrix x:Each element of Y is derivative of x, and then it is lined together to form a super matrix.Mathematical knowledge of

Source Address: http://blog.chinaunix.net/uid-26020768-id-3155898.html
1. Digital Image Processing, Gonzalez, Ma qiuqi, e-Industry Press;
2. opencv basics, Yu Shiqi, Liu Rui, Beijing University of Aeronautics and Astronautics Press;
3. Learning opencv computer vision with the opencv library, Gary bradski, Adrian kaebler, O 'Reilly
4. pattern recognition, Bian zha

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