popular machine learning algorithms

each of the above three memory regions each time it performs GC, and most of the time it refers to the new generation. So the GC has two different types in the reclaimed area, one for the normal GC (minor GC) and one for the global GC (major GC or full GC), and they are for the following areas.Normal GC (minor GC): GC for Cenozoic regions only.Global GC (major GC or full GC): GC for all generational regions (Cenozoic, old generation, permanent generations).Because the GC effect is not good for

algorithm to initially estimate the number of K.2) How to choose the initial K pointsThe common algorithm is random selection. But often the effect is not very good, also can be similar to the method, the line uses the hierarchical clustering algorithm to divide the K clusters, and uses these clusters ' centroid as the initial centroid.3) method of calculating distancesCommonly used such as European distance, cosine angle similarity degree.4) Algorithm Stop conditionThe maximum number of iterat

other.Suppose we choose the attribute R as the split attribute, DataSet D, R has K different values {v1,v2,..., Vk}, so d according to the value of R into K-group {d1,d2,..., Dk}, after splitting by R, the amount of information required to separate the different classes of DataSet D is:information gain is defined as before and after the split, two of the amount is only poor:The following example uses Python to illustrate a decision tree construct using the information gain method:The main steps

application thread exists in the contents of the set logs, and modify the corresponding remembered sets, this step needs to pause the application, parallel running.Survival Object calculation and cleanup ( Live Data counting and Cleanup )It should be noted that in G1, it is not that final marking pause is executed, it is certain to perform cleanup this step, because this step needs to suspend the application, G1 in order to achieve quasi-real-time requirements, It is necessary to reasonably pla

Logistic regression is used to classify, and linear regression is used to return.Linear regression is the addition of the properties of the sample to the front plus the coefficients. The cost function is the sum of squared errors. Therefore, in the minimization of the cost function, you can directly derivative, so that the derivative equals 0, as follows:Gradient descent can also be used to learn the same gradient as the logistic regression form.Advantages of linear regression: simple calculatio

Continuous update ...1.k-Nearest Neighbor algorithmAdvantages: High precision, insensitive to outliers, no data input settingsCons: High computational complexity, high spatial complexityApplicable data range: Numerical and nominal typeApplicable scenarios:2.ID3 Decision Tree AlgorithmAdvantages: The computational complexity is not high, the output is easy to understand, the missing middle value is not sensitive, can process the irrelevant characteristic dataDisadvantage: May cause over-matching

can be processed.Cons: Easy to fit.How to avoid overfitting:(1) dimensionality reduction, can use PCA algorithm to reduce the dimension of the sample, so that the number of theta of the model is reduced, the number of times will be reduced, to avoid overfitting;(2) regularization, the design of regular items regularization term.The regularization function is to prevent some properties before the coefficient weight is too large, there has been a fitting.Note that the way to resolve overfitting i

ObjectiveThis article only to some common optimization methods to introduce and simple comparison, the detailed contents and formulas of various optimization methods have to seriously chew the paper, here do not repeat.SGDSGD refers to Mini-batch gradient descent, about batch gradient descent, stochastic gradient descent, and mini-batch gradient descent The specific difference will not elaborate. SGD now generally refers to mini-batch gradient descent.SGD is the most common optimization method f

intention. Look at the judging criteria below. Using p to express precision,r expression recall; If we choose the criterion = (p+r)/2, then algorithm3 win, obviously unreasonable. Here we introduce an evaluation standard: F1-score. When p = or r=0, there is f=0; When P=1r=1, there is f=1, the largest; Similarly, we apply F1 score to the above three algorithms, and the results are ALGORITHM1 largest, which is the best; algorithm3 the least, the worst

belief Networks (DBN), convolutional networks (convolutional network), Stack-type Automatic encoder (stacked auto-encoders).2.12 Reducing the dimension of the algorithmLike the clustering algorithm, the reduced dimension algorithm tries to analyze the intrinsic structure of the data, but the reduced dimension algorithm attempts to use less information to summarize or interpret the data in an unsupervised learning way. Such

is a library that recognizes and standardizes time expressions. Stanford spied-Use patterns on the seed set to iteratively learn character entities from untagged text Stanford Topic Modeling toolbox-is a topic modeling tool for social scientists and other people who want to analyze datasets. Twitter text Java-java Implementation of the tweet processing library Mallet-Java-based statistical natural language processing, document classification, clustering, theme modeling, informat

introductory books. We recommend an article to further discuss this topic: "The best entry-level learning resources for machine learning". Related overview video: You can also watch some popular machine learning speeches. Exampl

analyzes the theoretical basis of evolutionary optimization for most evolutionary algorithms, which often depend on the insufficiency of heuristic algorithms. By drawing on the multi-layered framework of deep learning, Professor Chen Yu has developed hierarchical Bayesian analysis and online variable decibel Dean inference method in the 4th chapter. In the 5th c

model and re-experiment to optimize them. (ii) Criteria for numerical evaluation of machine learning algorithms 1. Cross-validation set error (accuracy) This is a good idea, the design of the fitting function if the cross-validation set test error is very large, then certainly not a good learning algorithm; However,

learning to organize the daily learning of machine learning algorithms, and practical problems, do more experiments, and strive to get a better learning effect, I will be firm belief, more efforts to catch up with the pace of exc

implementation.I explain this process as machine learning equals Matrix + statistics + optimization + algorithm . First, when the data is defined as an abstract representation, it often forms a matrix or a graph, which can be understood as a matrix. Statistics is the main tool and way of modeling, and the model solving is mostly defined as an optimization problem, especially, the frequency statistic method

classification, it is possible to know the approximate position of a feature. For example, detecting a cloud feature is likely to activate the upper part of the image. If activated in the lower half, the sheep may be detected. In the case of music recommendation, we usually only have some features in the music as a whole or a lack of interest, so it is reasonable to do the pooling in time.Another way to do this is to train the network with short audio clips, and get a longer fragment of data by

Dr. Hangyuan Li's "Talking about my understanding of machine learning" machine learning and natural language processing [Date: 2015-01-14] Source: Sina Weibo Hangyuan Li [Font: Big Small] Calculating time, from the beginning to the present, do m