machine learning book 2017

applied to the numerical attribute, for the ordinal attribute can be transformed to a numerical type, the nominal attribute normalization is also better, but the two-dollar attribute may not be very good. Main advantages and Disadvantages:Advantages: High accuracy, insensitive to noise, no data input assumptions requiredCons: High complexity of time and space, need to determine K value (k value determination may require a lot of experience)Here is the implementation of the KNN algorithm in the

About this article, my original blog address is located in http://blog.csdn.net/qq_37608890, this article from the author on December 06, 2017 18:06:30 written content (http://blog.csdn.net /qq_37608890/article/details/78731169). This article based on the recent Learning machine learning Books network articles, specia

data points, which involves the mapping of non-linear data to high-dimensional to achieve the purpose of linear divisible data.Support Vector Concepts: The above sample map is a special two-dimensional situation, of course, the real situation may be many dimensions. Start with a simple understanding of what a support vector is at a low latitude. Can see 3 lines, the middle of the red line to the other two first distance is equal. The red one is the hyper-plane that SVM looks for in two-di

************** written in the front ************** This article is a summary of the learning process and some personal ideas, only recorded, continuous update ... thin only sparse, I implore you if you find any problems please be sure to exchange a positive message Oh ~ ~ Personal opinion: The supervision study in machine learning can be divided into regression a

============================================================================================ "Machine Learning Combat" series blog is Bo master reading " Machine learning Combat This book's notes, including the understanding of the algorithm and the Python code implementation of the algorithmIn addition, bloggers here

Author: Thuy T. Pham Selected from the Heart of Kdnuggets Machine compilation participation: Wu Yu Artificial intelligence is becoming the basic technology for a new generation of technology change, but developing artificial intelligence programs for their applications and businesses from scratch is expensive and often difficult to achieve the performance they want, but fortunately we have a large number of Ready-to-use APIs available to use. These A

Absrtact: Recently in the "Machine learning actual Combat", in the process of code will always report some small errors, so the place of the debug; because it is jumping to see, so just a part of it, I hope that after the book I met all the errors are here to correct.Content:Nineth Chapter (regression tree): Mat0 = Dataset[nonzero (dataset[:,feature] >va

are 1+d, which is equivalent to the VC dimension of z space, so when Q becomes larger, the VC dimension becomes larger.Generalization problem (generalization Issue)We go back to machine learning is basically a balance between the compromise problem, if D (q), we can make ein very small, but this will lead to Ein and eout very different, when D (Q) small, can make Ein and eout difference small, but can not

Tools used: NumPy and MatplotlibNumPy is the most basic Python programming library in the book. In addition to providing some advanced mathematical algorithms, it also has a very efficient vector and matrix operations function. These are particularly important for computational tasks for machine learning. Because both the characteristics of the data, or the batch

), though it's no better than Microsoft's Visual Studio, but it's much more than the one that comes with it-if it's written in C, Helpless is written in Java, startup speed huge slow ~ ~Recently turned over the book "Machine Learning in Action". The book uses Python to implement some

. Naive Bayesian classifier has two kinds of polynomial model and Bernoulli model when it is used in text classification, and the algorithm realizes these two models and is used for spam detection respectively, which has remarkable performance.Note: Personally, the "machine learning Combat" naive Bayesian chapter on the text classification algorithm is wrong, whether it is its Bernoulli model ("word set") o

The book "Java Virtual machine concurrency programming" is really brief encounter. I've only scratched the surface of concurrent programming, and this book gives me a whole new understanding of concurrent programming. So put the knowledge points in the book to take notes, in order to review the use later.Concurrency an

angular regression and lasso Lars Description: How to find which function is provided by which package: http://cran.rstudio.com/->task views->machine learning-> Search "keyword, such as Lars"The execution code is as followsinstall.packages("lars"#http://cran.rstudio.com/ ->TASK Views->Machine Learning-

only in the limited target set value).Third, the algorithm example and explanationExamples in the case of "machine learning Combat" in the book, code examples are written in Python (need NumPy Library), but the algorithm, as long as the algorithm is clear, in other languages can be written out: Helen has been using online dating sites to find the right date fo

_ {M, 2 }} \ cdots \ frac {\ partial g (\ mathbf U) }{\ partial X _ {M, N }}\\ end {bmatrix }\\=\ left (\ sum _ {k} \ sum _ {L} \ frac {\ partial g (\ mathbf U )} {\ partial U _ {K, L }}\ frac {\ partial U _ {K, L }}{\ partial X _ {I, j }}\ right) _ {M \ times N }=\ left (tr \ left [\ left (\ frac {\ partial g (\ mathbf U) }{\ partial \ mathbf u} \ right) ^ {t} \ frac {\ partial \ mathbf u} {\ partial X _ {I, j }}\ right] \ right) _ {M \ times n} \] ? This article reposted self-China School

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

Recently, in the search for a solution to dimensionality, the following ideas have been found, which can be followed by this idea:Links: http://www.36dsj.com/archives/26723IntroductionIn 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 map

, for the supermarket cost table, False reject will sacrifice the cost is relatively large, and false accept sacrifice cost will be small. Therefore, we should try to avoid false reject situation.Examples of CIA fingerprint recognitionIf the CIA, use fingerprint identification to determine if the person has access to the system to view important information.Then, the occurrence of false accept will lead to very serious consequences, and false reject words, there will not be too much impact.There

Category: The meaning of classificationClassification in the traditional sense: biological speciesForecast: Weather ForecastDecision: Yes or noTraditional models of classificationWhat is the difference between classification (discriminant analysis) and clustering?Supervised learning, unsupervised learning, semi-supervised learningCommon classification models and algorithmsLinear discriminant methodDistance

Original address: http://www.mimno.org/articles/ml-learn/Written by David MimnoOne of my students recently asked me for advice on learning ML. Here's what I wrote. It ' s biased toward my own experience, but should generalize.My Current Favorite Introduction is Kevin Murphy's book (Machine learning). Might also want to