machine learning algorithms ppt

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

This week school things more so dragged a few days, this time we talk about clustering algorithm ha.First of all, we know that the main machine learning methods are divided into supervised learning and unsupervised learning. Supervised learning mainly refers to we have given

Roundtable", most of the real-life data is "living" in "high-dimensional space", and the simpler it is to deal with high-dimensional data, the more practical it is. With international academics like Martin introducing algorithms such as statistical machine learning to China, it is expected to accelerate the challenge of solving China's big data phenomena with ar

I. The idea of integrated learning methodThis paper introduces a series of algorithms, each of which has different scopes of application, such as dealing with linear variational problems, and dealing with linear irreducible problems. In the real world life, often because the "collective wisdom" makes the problem is easy to solve, then the problem, in machine

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

Original address: http://www.csuldw.com/2016/02/26/2016-02-26-choosing-a-machine-learning-classifier/This paper mainly reviews the adaptation scenarios and the advantages and disadvantages of several common algorithms!Machine learning algorithm too many, classification, regr

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

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

and is easily downloaded and modified by the reader.The following books will not be introduced, share the graphic coverHere is still to recommend my own built Python development Learning Group: 725479218, the group is the development of Python, if you are learning Python, small series welcome you to join, everyone is the software Development Party, not regularly share dry goods (only Python software develo

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:

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

)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

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

classes more equal. but .....Recall, though,that better data often beats better algorithms, and designing good features goes a long. And if you had a huge dataset, your choice of classification algorithm might not really matter so much in terms of Classi Fication performance (so choose your algorithm based on speed or ease of use instead).And if you really-accuracy, you should definitely try a bunch of different classifiers and select the best one by

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

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