list of machine learning models

Content Summary The main content of this blog is:1. Model Selection2. Bayesian statistics and Regulation (Bayesian statistics and regularization) The core is the choice of the model, although not so many complex formulas, but he provides more macro guidance, and many times is essential. Now let's begin model selection Suppose we train different models to solve a learning problem, such as we have a polynomi

-driven methods to write and run tests before you write codelearn the best usage of eight machine learning algorithms and weigh themtest each algorithm by hands-on real-world examplesUnderstanding the similarity between test-driven development and the scientific approach to validating solutionsLearn about the risks of machine

Naive BayesianThis course outline:1. naive Bayesian- naive Bayesian event model2. Neural network (brief)3. Support Vector Machine (SVM) matting – Maximum interval classifierReview:1. Naive BayesA generation learning algorithm that models P (x|y).Example: Junk e-mail classificationWith the mail input stream as input, the output Y is {0,1},1 as spam, and 0 is not j

. Most machine learning algorithms involve training and predicting two parts, training models, and predicting unknown samples. The same is true for machine learning packages in spark.Spark divides the machine

　In this article we will outline some popular machine learning algorithms.Machine learning algorithms are many, and they have many extensions themselves. Therefore, how to determine the best algorithm to solve a problem is very difficult.Let us first say that based on the learning approach to the classification of the

Based on the literal Relevance Model of Baidu keyword search recommendation tool, this article introduces the specific design and implementation of a machine learning task. Including target setting, training data preparation, feature selection and filtering, and model training and optimization. This model can be extended to Semantic Relevance models, and the desi

predict multiple output variables for each input instance. This differs from the case where only one single target variable is involved in the "normal" case. In addition, Meka is based on the Weka Machine Learning Toolkit. 4. Advanced Data Mining and machine learning System (ADAMS) is a new type of flexible workflow e

Fusion Models (Aggregation model)If we've got some features or assumptions, and they have some consistency with our goal of machine learning, we can combine these assumptions to make predictions better, such models are called fusion models.A fusion model is a way to get better predictions by mixing (mix) and combining

The ability to give computer learning dataCover:1. General concepts of machine learning2. Three types and basic terminology of machine learning methods3. Modules required to successfully build a machine learning systemThree differ

the journal, a year or two after the publication of a little out. As a result, most of the latest work is first published in top-level meetings that reflect the "hot research direction" and "the latest approach". (2) A lot of classical work may lead to a paper in a top-level periodical, because the journal paper is more complete and full of experiments. But many are actually starting at the top of the conference. such as pLSA, latent Dirichlet allocation and so on. (3) If you pay attention to t

Http://blog.sina.com.cn/s/blog_6b99cdb50101ix0l.htmlOne of the math related to machine learning and computer vision(The following is a space article to be transferred from an MIT bull, which is very practical:)DahuaIt seems that mathematics is not always enough. These days, in order to solve some of the problems in the library, also held a mathematical textbook. From the university to the present, the class

is easier to apply in Robot Control and other control systems. Similarity Algorithm Algorithms generally present similarity in functions or forms. For example, the tree-based method and neural network method are inspired. This is a useful grouping method, but it is imperfect. There are still some algorithms that are easy to integrate into multiple categories, such as learning vector quantization, which is both a neural network-inspired method and an

is written in pure Java. 18. N-Dimensional Arrays for Java (ND4J) is a scientific computing library for JVM. They are used in the production environment, which indicates that the routine is designed to run with minimal memory requirements. 19. Java Machine Learning LibraryJava Machine Learning Library) is the implemen

This paper mainly records the cost function of neural network, the usage of gradient descent in neural network, the reverse propagation, the gradient test, the stochastic initialization and other theories, and attaches the MATLAB code and comments of the relevant parts of the course work. Concepts of neural networks, models, and calculation of predictive classification using forward propagation refer to Andrew Ng

learning require a data-set, which contains sample examples. These samples contain many characteristics, and the task of machine learning in many cases is to learn the characteristics of datasets. Unsupervised learning requires a unique algorithm to learn features, common algorithms include clustering Algorithms (Clus

control of machine learning challenges, we will ensure that more products are used TensorFlow.Today, atGCP NEXT 2016, weAnnounced the alpha release ofCloud Machine Learning, a framework for building and training custom models to is used in intelligent applications.today, at

This article is a computer Quality Pre-sale recommendation >>>>Spark machine learningWhen machine learning meets the most popular parallel computing framework spark ...Editor's recommendationApache Spark is a distributed computing framework optimized to meet the needs of low latency tasks and memory data storage.Apache Spark is a rare framework in the existing pa

, that is, our training error will be very small. But the small training error is not our ultimate goal, our goal is to hope that the model test error is small, that is, to accurately predict new samples. Therefore, we need to ensure that the model is "simple" based on the minimization of training errors, so that the resulting parameters have good generalization performance (that is, the test error is also small), and the model "simple" is the rule function to achieve. In addition, the use of ru

would sort an array. Perform an indirect sort along the given axis using the algorithm specified by the kind keyword. It returns an array of indices of the same shape as a , the index data along the given axis in sorted order. Returns an array of subscripts after a small to large order. Axis represents the dimension to compare, which defaults to the last dimension.　Some function learning in 2.pythonThe reload () function, which needs to be i

, i.e. gt,i=gt,i+n (0,σ2t) The variance of the Gaussian error requires annealing: σ2t=η (1+t) γ increasing the random error on the gradient increases the robustness of the model, even if the initial parameter values are not chosen well and is suitable for training in a particularly deep-seated network. The reason for this is that increasing random noise is more likely to jump over local extreme points and find a better local extremum, which is more common in deep networks. Summary in the above