bias book

OverviewThe bias lock is a lock optimization method proposed by JDK 1.6, and the core idea is that if the program does not compete, the synchronization of the thread that has already taken the lock is canceled. In other words, when a lock is acquired by a thread, it enters the biased lock mode, and when the thread requests the lock again, there is no need to synchronize the operation, thus saving the operation time. However, if there are other threads

training data is fully fit, these random noises will also be fit in), resulting in the model being too complex, and it is very likely that the performance of the new dataset is very poor, called Over-fitting ). Bias-Variance Decomposition is a statistical view of the complexity of the model. The details are as follows: Suppose we have K datasets, each of which is extracted independently from a distributed p (t, x) (t represents the variable to be pre

Derivation and derivative operation in mathematics is a very important part, especially in higher mathematics, basically by the function of derivative and biased composition, a lot of formula theorem is also about this, if less this part, mathematics will be eclipsed. Therefore, when it comes to these contents in the document, it is inevitable that the derivation of derivative symbols will appear, then when editing the formula, how to express theMathType two-time

Opencv-Bias and Gain // define head function#ifndef PS_ALGORITHM_H_INCLUDED#define PS_ALGORITHM_H_INCLUDED#include #include #include "cv.h"#include "highgui.h"#include "cxmat.hpp"#include "cxcore.hpp"#include "math.h"using namespace std;using namespace cv;void Show_Image(Mat, const string );#endif // PS_ALGORITHM_H_INCLUDED/*Adjust bias and gain.*/#include "PS_Algorithm.h"float

One: Java multi-threaded mutex, and Java multithreading introduce the reason for the bias lock and lightweight lock? --->synchronized of the weight of the lock, that is, when the thread runs to the code block, the program's running level from the user state to the kernel state, to suspend all the threads, so that the CPU through the operating system instructions, to dispatch multithreading between, who execute code block, who enters the blocking state

CLC Clear all; Close all; Addpath (' E:\PhotoShop algortihm\image processing\ps algorithm '); Image=imread (' 4.jpg '); Image=double (Image)/255;% imshow (Image) % Set the gain value 0-1 % Set the bias value 0-1gain=0.5; bias=0.25;% adjust the gainp=Log(1-gain)/Log(2.W); sz=size(Image); T1=image (:); T1 (t10.001)=0; T1 (t1>0.999)=1.0; ind_1=Find(t1>0.001); Ind_2=ind_1 (Find(T1 (Ind_1) 0.5));

Reference: https://codesachin.wordpress.com/2015/08/05/on-the-biasvariance-tradeoff-in-machine-learning/I've never figured out what bias is, what is variance, and now look at this blog post.When your model is too simple, that is, your train error is too big, your bias will be larger, and when your model becomes complex, bias becomes smaller, and the models become

phenomenon, we might want to open a multimedia file and perform the Color Correction menu command in a multimedia playback state, and then select the overlapping/VMR item in the corresponding Settings page, "Apply color changes to" settings, Then try using manual method to adjust the brightness switch, contrast switch in the monitor Control panel so that the brightness and contrast values of the display can be increased properly until the monitor is working properly in that state. 2, to avoid t

Conclusion ↑bias↓variance↓ of model complexity Example $y _i=f (x_i) +\epsilon_i,e (\epsilon_i) =0,var (\epsilon_i) =\sigma^2$Using KNN to make predictions, excepted prediction error at point $x_0$:$EPE (X_0) =e\left[\left (Y_0-\hat{f} (X_0) \right) ^2|x_0\right]\\ \ \ =e\left[\left (Y_0-E (y_0) \right) ^2|x_0\right]+\ Left[e (\hat{f} (X_0))-E (Y_0) |x_0\right]^2+e\left[\hat{f} (X_0)-E (\hat{f} (x_0)) \right]^2\\ \ \ =\sigma^2+{

Http://blog.csdn.net/xum2008/article/details/38147425) Hidden Markov Model (HMM ): Figure 1 Hidden Markov Model Disadvantages of Hidden Markov Model: 1. Hmm only depends on each State and its corresponding observed object: Sequence labeling is not only related to a single word, but also to the length of the observed sequence, the context of the word, and so on. 2. the target function does not match the prediction target function: Hmm learns the joint distribution of State and observation sequen