machine learning techniques and algorithms

-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,

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

project applications. In this paper, we only discuss the space-time complexity and parallelism of various algorithms.Evaluation criteriaThe application of machine learning algorithms is usually taken offline after the model is trained. Put it on the line to predict. for server clusters. It is possible that training and prediction occur on the same device. But in

above question, we can apply the kernel function:Quadratic coefficient q n,m = y n y m z n T z m = y n y m K (x N, x m) to get the Matrix Qd.So, we need not to de the caculation in space of Z, but we could use KERNEL FUNCTION to get znt*zm used xn and XM.Kernel Trick:plug in efficient Kernel function to avoid dependence on d?So if we give the This method a name called Kernel SVM:Let us come back to the 2nd polynomial, if we add some factor into expansion equation, we may get some new kernel fun

technology. 5 (3), 2014[3] Jerry lead http://www.cnblogs.com/jerrylead/[3] Big data-massive data mining and distributed processing on the internet Anand Rajaraman,jeffrey David Ullman, Wang Bin[4] UFLDL Tutorial http://deeplearning.stanford.edu/wiki/index.php/UFLDL_Tutorial[5] Spark Mllib's naive Bayesian classification algorithm http://selfup.cn/683.html[6] mllib-dimensionality Reduction http://spark.apache.org/docs/latest/mllib-dimensionality-reduction.html[7] Mathematics in

the WTW:The essence is similar.Another understanding: If we consider the constraints in SVM as a filtering algorithm, for a number of points in a plane,It is possible that some margin non-conforming methods will be ignored, so this is actually a reduction of the problem of the VC dimension, which is also an optimization direction of the problem.With the condition of M > 1.126, better generalization performance was obtained compared to PLA.Taking a circle midpoint as an example, some partitionin

under-fitting with verification curveValidating a curve is a very useful tool that can be used to improve the performance of a model because he can handle fit and under-fit problems.The verification curve and the learning curve are very similar, but the difference is that the accuracy rate of the model under different parameters is not the same as the accuracy of the different training set size:We get the validation curve for parameter C.Like the Lea

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 popular

Machine learning is a core skill of the data analyst advanced Step. Share the article about machine learning, no algorithms, no code, just get to know machine learning quickly!---------

the superset of that element are infrequent. The Apriori algorithm starts with a single-element itemsets and forms a larger set by combining itemsets that meet the minimum support requirements. The degree of support is used to measure how often a collection appears in the original data.2.10 Fp-growth algorithm:Description: Fp-growth is also an algorithm for discovering frequent itemsets, and he uses the structure of the FP tree to store building elements, and other apriori

This article is a translation of the article, but I did not translate the word by word, but some limitations, and added some of their own additions.Machine Learning (machines learning, ML) is what, as a mler, is often difficult to explain to everyone what is ML. Over time, it is found to understand or explain what machine lea