list of machine learning algorithms

to the existing data, the classification boundary line is established, and then the regression formula is classified.Advantages: Simple implementation, easy to understand and implement, low computational cost, fast speed, lower storage resources;Disadvantages: easy to fit, classification accuracy may not be highem expectation maximization algorithm-God algorithm as long as there are some training data, and then define a maximization function, using the EM algorithm, the computer through a numbe

This paper mainly includes the realization of common machine learning algorithms, in which the mathematical derivation, principle and parallel implementation will give the link. Machine Learning (machines learning, M

Machines (SVM), referred to as the SV Machine (the general abbreviation in the paper). It is a supervised learning method, which is widely used in statistical classification and regression analysis. Support Vector machines map vectors to a higher dimensional space, where a maximum interval of hyperspace is established in this space. On both sides of the super plane that separates the data, there are two su

1 Scenario Resolution: A. Data exploration (size of data, missing or garbled data, ETL operation, field type, whether or not the target queue is included)B. Scene abstraction (it is through the existing data, to dig out the business scenarios can be applied.) Machine learning is primarily used to address scenarios including two classification, multi-classification, clustering, and regression.C. Algorithm se

of experience. It is characterized by the use of past experience in the solution of problems, the selection of methods that have been effective, rather than the systematic and determined steps to seek answers. There are many kinds of heuristic optimization methods, including classical simulated annealing method, genetic algorithm, ant colony algorithm, particle swarm algorithm and so on.There is also a special optimization algorithm called multi-Objective optimization algorithm, which is mainly

value;If it becomes smaller, the new puzzle will replace the original;If it becomes larger, the probability of replacing the old one with the new one depends on the current temperature value, where the temperature will begin to slow down at a relatively high value, which is why the algorithm is more receptive to relatively poor performance in the early stages of execution, so that we can effectively avoid the possibility of falling into the local minimum, when the temperature reaches 0, The alg

Introduction to several common optimization algorithms for machine learning789491451. Gradient Descent method (Gradient descent) 2. Newton's method and Quasi-Newton method (Newton ' s method Quasi-Newton Methods) 3. Conjugate gradient method (conjugate Gradient) 4. Heuristic Optimization Method 5. Solving constrained optimization problems--Lagrange multiplier methodEach of us in our life or work encountere

introductionThe basic SVM classifier solves the problem of the 2 classification, the case of N classification has many ways, here is introduced 1vs (n–1) and 1v1. More SVM Multi-classification application introduction, reference ' SVM Multi-Class classification method 'In the previous method we need to train n classifiers, and the first classifier is to determine whether the new data belongs to the classification I or to its complement (except for the N-1 classification of i). The latter way we

the depth of decision tree(2) The structure of the tree changes due to a little change in the sample, which can be improved by integrated learning.Application:(1) Financial options for option pricing are of great use(2) Remote sensing is the application field of pattern recognition based on decision Tree(3) Banks use decision tree algorithm to classify the probability of default payment by loan applicant(4)Gerber Products Inc., a popular baby products company, uses decision tree

For the following three reasons, we chose python as the programming language for implementing machine learning algorithms: (1) Clear Python syntax; (2) Easy to operate plain text files; (3) widely used, there are a large number of development documents. Executable pseudocode Python has a clear syntax structure and is also called executable pseudo-code ). The defa

, activating the back of the nerve layer, the final output layer of the 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

Original: http://dataunion.org/13451.htmlXbinworld Introduction:In the field of machine learning, the so-called dimensionality reduction refers to the mapping of data points in the original high-dimensional space to the low-dimensional space. The essence of dimensionality is to learn a mapping function f:x->y, where x is the expression of the original data point, which is currently used at most in vector re

machine Learning Algorithms Summary 1. Preface by using the machine learning algorithm to summarize the work, convenient for later search, rapid application. 2. Recommended Algorithms Cross Minimum Variance

conjugate gradient method is not only one of the most useful methods to solve the large scale linear equations,is also one of the most effective algorithms for solving large-scale nonlinear optimization. In various optimization algorithms, the conjugate gradient method is very important. Its advantage is that the required storage capacity is small, has step convergence, high stability, and does not require

Machine learning algorithms are numerous, and various algorithms involve more parameters, this article will briefly introduce the RF,GBDT and other algorithms of tuning experience and steps. 1. BP Tuning matters1.BP is sensitive to feature scaling, first scale data.2. Experi

of the total number of features with non-0 weights)9. Logistic regression : Two-dollar category, extremely efficient Giallo Computer System (many problems need to use probability estimates as output) two ways: "As is" "converted to two-dollar category" Application: Automatic diagnosis of disease (to investigate the risk factors that cause disease, and to predict the probability of disease occurrence according to risk factors), economic forecasts and other fieldsCategory: Evaluation indicators:

)Feature Selection (Feature selection algorithm):Mutual information (Mutual information), Documentfrequence (document frequency), information Gain (information gain), chi-squared test (Chi-square test), Gini (Gini coefficient).Outlier Detection (anomaly detection algorithm):Statistic-based (based on statistics), distance-based (distance based), density-based (based on density), clustering-based (based on clustering).Learning to Rank (based on

specific job requirements, image algorithm For example, now deep learning hot not I said, so the basic convolution neural network algorithm , image classification , image detection The more famous paper in recent years should read it. If you have a condition, use it like a caffe,tensorflow frame.2. Machine Learning EngineerThis post is basically the same as the

paper is usually European-style distance, Pearson coefficient or cosine similarity.Assuming that a matrix A is established, the M*n matrix, the rows are all users, n is all items, each element of the matrix represents the user's rating of the item, then the item-based or user-based recommendation is to calculate the similarity of all columns or all rows. In real life, this matrix is very sparse.Topic: Recommend users to buy TOPN itemsThe Matrix C is a m*n matrix, each row represents each user,

KNN algorithm of ten Algorithms for machine learningThe previous period of time has been engaged in tkinter, machine learning wasted a while. Now want to re-write one, found a lot of problems, but eventually solved. We hope to make progress together with you.Gossip less, get to the point.KNN algorithm, also called near