list of machine learning models

discriminant models (discriminative model)The generation method is obtained by the data Learning Joint probability distribution P (x, y) and then the conditional probability distribution P (y| X) as the predictive model, the model is generated : P (Y |X )= P(X,Y)p ( X ) This method is called a build method , which represents the generation relationship of output y produced by a given

integration algorithm is how to integrate the independent weak learning models and how to integrate the learning results. This is a very powerful algorithm, but also very popular. Common algorithms include: Boosting, bootstrapped Aggregation (Bagging), AdaBoost, stacking generalization (stacked generalization, Blending), gradient pusher (Gradient Boosting

AsE2 IfE1 is negative, select the maximumEi AsE2 , usually for each sample of theEi Save in a list, select the largest| E1? E2| To approximate the maximum step size. 4 calculation of the threshold BAfter each optimization of the two variables, the threshold B is updated as it relates to the calculation of f (x), which is related to the next optimization.This part concludes that the optimal equation F (x) =ωtx + B is obtained by the value of the

values of each eigenvalue have the same scale range, so that the influence of each eigenvalue is the same.How do I set the value of λ? By selecting a different λ to repeat the test process, a λ that minimizes the prediction error is obtained. The best value can be obtained by cross-validation-the sum of squared errors is minimized on the test data.Ridge regression was first used to deal with more than a sample number of features, and is now used to add human bias to the estimate, thus obtaining

from:http://blog.jobbole.com/60809/After understanding the machine learning problems that we need to solve, we can think about what data we need to collect and what algorithms we can use. In this article, we'll go through the most popular machine learning algorithms and get a general idea of which methods are available

what type of service a vehicle may need. Another interesting machine learning use case is predicting stock market volatility based on previous stock earnings records. A recent study has shown that using machine learning to predict the stock market has more than 60% accuracy. In the area of medical health,

Original address: Http://www.demnag.com/b/java-machine-learning-tools-libraries-cm570/?ref=dzoneThis is a list of the Java machine learning tools libraries. Weka have a collection of machine

stepped on a lot of pits, here and we share a few I think the bigger pit, I hope to be helpful to everyone. I'll introduce a few pits first, and then we'll talk about the feeling and the harvest that we crawled out of the pit.See the model, not the system. If we were to put a name on the pit we had stepped on, the pit must be the first place. Because if you fall into this hole, then the basis for directing your system's direction is probably completely wrong.Specifically, the problem is that wh

This section describes the core of machine learning, the fundamental problem-the feasibility of learning. As we all know about machine learning, the ability to measure whether a machine learni