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Original: http://blog.csdn.net/abcjennifer/article/details/7797502This column (machine learning) includes linear regression with single parameters, linear regression with multiple parameters, Octave Tutorial, Logistic Regression, regularization, neural network, design of the computer learning system, SVM (Support vecto
1.1 machine learning basics-python deep machine learning, 1.1-python
Refer to instructor Peng Liang's video tutorial: reprinted, please indicate the source and original instructor Peng Liang
Video tutorial: http://pan.baidu.com/s/
Abu-mostafa is a teacher of Lin Huntian (HT Lin) and the course content of Lin is similar to this class.L 5. 2012 Kaiyu (Baidu) Zhang Yi (Rutgers) machine learning public classContent more suitable for advanced, course homepage @ Baidu Library, courseware [email protected] Dragon Star ProgramL prml/Introduction to machine le
(Projection Pursuit), and more.1.3.12Integration AlgorithmsThe integrated algorithm trains the same sample independently with some relatively weak learning models, then integrates the results for overall prediction. The main difficulty of integration algorithm is how to integrate the independent weak learning models and how to integrate the learning results. Thi
, the ascending dimension, the formation of non-linear machine learning polynomial, and the polynomial, but also can be expressed as a matrix vector, if the periodic function can be expressed by the Taylor Formula trigonometric functions, that is, the famous Fourier transform, so ultimately, polynomial convex function, optimization problem, and polynomial fitting in prediction; common fitting with logistic
to do it?
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Take-home lessons.
You ' ll learn how to:
Identify Basic theoretical principles, algorithms, and applications of machine learning
Elaborate on the connections between theory and practice in machine learning
Master the mathematical and heuristic aspects of
I. About the origins of the boosting algorithmThe boost algorithm family originates from PAC learnability (literal translation called Pac-Learning). This set of theories focuses on when a problem can be learned.We know that computable is already defined in computational theory, and that learning is what the PAC learnability theory defines. In addition, a large p
integration algorithm is how to integrate the independent weak learning models and how to integrate the learning results. This is a very powerful algorithm, but also very popular. Common algorithms include: Boosting, bootstrapped Aggregation (Bagging), AdaBoost, stacking generalization (stacked generalization, Blending), gradient pusher (Gradient
(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
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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
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