boosting machine learning tutorial

(learning vector quantization, LVQ), and self-organizing mapping algorithm (self-organizing map, SOM)Regularization method　　The regularization method is the extension of other algorithms (usually the regression algorithm), which adjusts the algorithm according to the complexity of the algorithm. The regularization method usually rewards the simple model and punishes the complex algorithm. Common algorithms include: Ridge Regression, Least Absolute Sh

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 least squares regression (partial Least Square regression,pls), Sammon mappings, Multidimens

data that are not identified. Common depth learning algorithms include: Restricted Boltzmann machines (Restricted Boltzmann machine, RBN), deep belief Networks (DBN), convolutional networks (convolutional network), Stack-type Automatic encoder (stacked auto-encoders).Reduce the dimension of the algorithmLike the clustering algorithm, the reduced dimension algorithm tries to analyze the intrinsic structure

Find a good article on the internet, paste it directly, add some supplements and your own understanding, and count as this article. My education in the fundamentals of machine learning has mainly come from Andrew Ng's excellent Coursera course on the topic. one thing that wasn't covered in that course, though, was the topic of "Boosting" which I 've come into SS

make an overall prediction. This kind of algorithm is also called meta-algorithm (META-ALGORITHM). The most common ideas for integration are two bagging and boosting.boostingBuild new classifiers and integrate them based on error-boosting classifier performance by focusing on samples that have been categorized incorrectly by existing classifiers.BaggingClassifier construction method based on random resampling of data.Algorithm Example:

the regression algorithm) , which adjusts the algorithm according to the complexity of the algorithm. The regularization method usually rewards the simple model and punishes the complex algorithm. common algorithms include: Ridge Regression, Least Absolute Shrinkage and Selection Operator (LASSO), and elastic networks (Elastic Net).Decision Tree Learning　　Decision Tree algorithm uses tree structure to establish decision-making model according to the

advantageous in many ways than in previous systems based on artificial rules. The artificial neural network at this time, although also known as Multilayer perceptron (multi-layer Perceptron), is actually a shallow layer model with only one layer of hidden layer nodes. In the the 1990s, a variety of shallow machine learning models were presented, such as support vector machines (svm,support vector machines

-supervised learning, which is a problem with only a small amount of data in a large data set. Restricted Boltzmann Machine (RBM) Deep belief Networks (DBN) Convolutional Network Stacked Auto-encoders dimensionality reduction (dimensionality Reduction)Similar to the clustering method, the inherent structure in the data is utilized, and unsupervised methods are used to learn a way t

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 least squares regression (partial Least Square regression,pls), Sammon mappings, Multidimensional scales (multi-dimensional scaling, MDS), projection tracking (Projection Pursuit), etc.In

statistical-based machine learning approach is more advantageous in many ways than in previous systems based on artificial rules. The artificial neural network at this time, although also known as Multilayer perceptron (multi-layer Perceptron), is actually a shallow layer model with only one layer of hidden layer nodes.In the the 1990s, a variety of shallow machine