list of machine learning algorithms

In the introduction of recommendation system, we give the general framework of recommendation system. Obviously, the recommendation method is the most core and key part of the whole recommendation system, which determines the performance of the recommended system to a large extent. At present, the main recommended methods include: Based on content recommendation, collaborative filtering recommendation, recommendation based on association rules, based on utility recommendation, based on knowledge

Machine learning Algorithms and Python Practice (ii) Support vector Machine (SVM) BeginnerMachine learning Algorithms and Python Practice (ii) Support vector Machine (SVM) Beginner[Emai

A bunch of online searches, and finally the links and differences between these concepts are summarized as follows: 1. Data mining: Mining is a very broad concept. It literally means digging up useful information from tons of data. This work bi (business intelligence) can be done, data analysis can be done, even market operations can be done. Using Excel to analyze the data and discover some useful information, the process of guiding your business through this information is also the process of

Reprinted from: Http://www.cnblogs.com/shishanyuan/p/4747761.html?utm_source=tuicool1. Machine Learning Concept1.1 Definition of machine learningHere are some definitions of machine learning on Wikipedia:L "Machine

classification method, but it is not perfect. Some algorithms can easily be classified into several categories, such as learning vector quantization, which is both a neural network-inspired method and an instance-based method. There are also some algorithms whose names describe both the problems they are dealing with and the names of a specific type of

Self-study machine learning three months, exposure to a variety of algorithms, but many know its why, so want to learn from the past to do a summary, the series of articles will not have too much algorithm derivation.We know that the earlier classification model-Perceptron (1957) is a linear classification model of class Two classification, and is the basis of la

understand the task, so "save the Earth" to understand "kill all human beings." This is like a typical predictive algorithm that literally understands the task and ignores the other possibilities or the practical significance of the task.So, in January 2016, Harvard Business School professor Michael Luca, professor of economics Sendhil Mullainathan, and Cornell University professor Jon Kleinberg, published an article titled "Algorithm and Butler" in the Harvard Commercial Review. Call upon the

Here are some general basics, but it's still very useful to actually do machine learning. As the key to the application of machine learning on current projects such as recommender systems and DSPs, I think data processing is very important because in many cases, machine

[x] * w + interaction# calculate the predicted output loss = Sigmoid (classlabels[x] * p[0, 0])-1 Print loss w_0 = W_0-alpha * loss * Classlabels[x] for i in Xrange (n): If datamatrix[x, I]! = 0:w[i, 0] = w[i, 0]-alpha * loss * classlabels[x] * datamatrix[x, I] for j in Xrange (k): V[i, j] = V[i, j]-alpha * loss * CLASSLABELS[X] * (data Matrix[x, i] * inter_1[0, J]-V[i, j] * datamatrix[x, i] * datamatrix[x, I]) return w_0, W, Vdef Getaccura Cy (Datamatrix, Classlabels, W_0, W, v):

-domains, such as "machine learning", "Data mining", "Pattern recognition", "Natural language processing" and so on. These sub-areas may have intersections, but the focus is often different. For example, "machine learning" is more focused on algorithmic aspects. In general, "artificial intelligence" is a subject area,

classification problem, conversely, if y is a continuous real number, this is a regression problem.Given a set of sample characteristics S={x∈rd}, we do not have a corresponding y, but want to explore the set of samples in the D-dimensional distribution, such as the analysis of which samples are closer, which samples are far away, this is a clustering problem.If we want to use the subspace with lower dimensionality to represent the original high-dimensional feature space, then this is the dimen

Summaryhave been interested in machine learning, has no time to study, today is just the weekend, have time to see the major technical forum, just see a good machine learning article, here to share to everyone.Machine learning is undoubtedly a hot topic in the field of curre

1. Integrated Learning OverviewIntegrated learning algorithm can be said to be the most popular machine learning algorithms, participated in the Kaggle contest students should have a taste of the powerful integration algorithm. The integration algorithm itself is not a separ

specific flow of the Lle algorithm is as follows (source: machine Learning Zhou Zhihua version)　　　　Lle Algorithm Summary:Key Benefits:1) can learn the local linear low-dimensional manifold of any dimension2) The algorithm comes down to the sparse matrix feature decomposition, the computational complexity is relatively small, the realization is easy.3) can deal with non-linear data, can be non-linear dimens