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
space corresponds to a feature. Sometimes it is assumed that the input space and the feature space are the same space, they are not differentiated, sometimes it is assumed that the input space and the feature space are different spaces, the instance is mapped from the input space to the feature space. The model is actually defined on the feature space. This provides a good basis for the classification of machine
Machine learning is divided into: supervised learning, unsupervised learning, semi-supervised learning (can also be used Hinton said reinforcement learning) and so on.
Here, the main understanding of supervision and unsu
nodes on the node on behalf of a variety of fractions, example to get the classification result of Class 1The same input is transferred to different nodes and the results are different because the respective nodes have different weights and biasThis is forward propagation.10. MarkovVideoMarkov Chains is made up of state and transitionsChestnuts, according to the phrase ' The quick brown fox jumps over the lazy dog ', to get Markov chainStep, set each word to a state, and then calculate the prob
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optimization of the hyper-parameters. The following algorithms are used primarily:Classification:· Random Forest· Gbm· Logistic Regression· Naive Bayes· Support Vector Machines· K-nearest NeighborsRegression:· Random Forest· Gbm· Linear Regression· Ridge· Lasso· SvrWhat parameters should I optimize? How can I select the most matching parameters? This is the two problems that people think about the most. It is not possible to answer this question wit
Regression, PLS), Sammon Mapping, multidimensional scale ( multi-dimensional scaling, MDS), projection tracking ( Projection Pursuit), and more. 1.3.12 Integration AlgorithmThe integrated algorithm trains the same sample independently with some relatively weak learning models, then integrates the results for overall prediction. The main difficulty of integration algorithm is how to integrate the independent weak
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
(Projection Pursuit), and more.1.3.12Integration AlgorithmsThe integrated algorithm trains the same sample independently with some relatively weak learning models, then integrates the results for overall prediction. The main difficulty of integration algorithm is how to integrate the independent weak learning models and how to integrate the learning results. Thi
parallel. However, partial parallelism can be achieved by self-sampling SGBT.8, GBDTAdvantages: 1, can flexibly deal with various types of data, including continuous and discrete values, processing classification and regression problems, 2, in the relatively few parameters of the time, the forecast preparation rate can also be relatively high. This is relative to the SVM, 3, can be used to filter features.4, using some robust loss function, the robustness of outliers is very strong. such as Hub
is all 0. And because it can be deduced that b=1nz∗zt=wt∗ (1NX∗XT) w=wt∗c∗w, this expression actually means that the function of the linear transformation matrix W in the PCA algorithm is to diagonalization the original covariance matrix C. Because diagonalization in linear algebra is obtained by solving eigenvalue and corresponding eigenvector, the process of PCA algorithm can be introduced (the process is mainly excerpted from Zhou Zhihua's "machine
network, clustering and so on. See here everyone should understand, "neural network" is just "machine learning" one of the many algorithms. In the various algorithms of machine learning, it is possible that with the change of tim
python is an object-oriented, interpretive computer programming language with a rich and powerful library, coupled with its simplicity, ease of learning, speed, open source free, portability, extensibility, and object-oriented features,python Become the most popular programming language of the 2017! AI is one of the most popular topics,
the reduced dimension algorithm attempts to use less information to summarize or interpret the data in an unsupervised learning way. Such algorithms can be used to visualize high-dimensional data or to simplify data for supervised learning. Common algorithms include: PCA (Principle Component Analysis, PCA), Partial le
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
python is an object-oriented, interpretive computer programming language with a rich and powerful library, coupled with its simplicity, ease of learning, speed, open source free, portability, extensibility, and object-oriented features,python Become the most popular programming language of the 2017! AI is one of the hottest topics, and machine
After learning about the types of machine learning problems to be solved, we can start to consider the types of data collected and the machine learning algorithms we can try. In this post, we will introduce the most
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