machine learning techniques and algorithms

Machine Learning (machines learning, ML) is a multidisciplinary interdisciplinary subject involving probability theory, statistics, approximation theory, convex analysis, algorithmic complexity theory and many other disciplines. Specialized in computer simulation or realization of human learning behavior, in order to a

entered machine learning will encounter two problems when they are faced with the basic learning of Mathematics: I don't know what mathematical knowledge is needed in machine learning and deep learning. Can not reall

Transferred from: http://www.cnblogs.com/data2value/p/5419864.htmlThis list summarizes 25 Java machine learning tools libraries:1. Weka integrates a machine learning algorithm for data mining work. These algorithms can be applied directly to a dataset or you can write your

algorithm, deep learning summarizes three kinds of neural networks.Supervised learningSupervised learning, as shown below, introduces a very large number of basic concepts, including loss function, gradient descent, and maximum likelihood estimation. The loss function shows the commonly used least squares loss function, the folding loss function and the cross entropy loss function, and the image, definitio

using K-fold cross-validationA key step in building a machine learning model is to evaluate the performance of the model on new data.Common cross-validation techniques: holdout cross-validation and K-fold cross-validation.Holdout cross-validationHoldout cross-validation is a classic and common method for evaluating the generalization performance of

sequence can be combined with the semantic representation of an image or video. As mentioned above, you can think of this bonding process as converting from one mode to another, or comparing the semantics of two modes. This is how Google Image search works at the moment. Q: I am writing an undergraduate thesis on the philosophical aspects of science and logic. In the future I would like to transfer to the computer department for my master's degree and then my PhD in

Original address: Http://www.demnag.com/b/java-machine-learning-tools-libraries-cm570/?ref=dzoneThis is a list of the Java machine learning tools libraries. Weka have a collection of machine learning

How to evaluate the assumptions we get from our learning algorithms and how to prevent overfitting and less-fitting problems.When we determine the parameters of the learning algorithm, we consider the choice of parameters to minimize the training error. Some people think that getting a small training error must be a good thing. But in fact, just because this hypo

Machine learning is divided into supervised machine learning, unsupervised machine learning, and semi-supervised machine learning. The crite

(i) Recognition of the returnRegression is one of the most powerful tools in statistics. Machine learning supervised learning algorithm is divided into classification algorithm and regression algorithm, in fact, according to the category label distribution type is discrete, continuity and defined. As the name implies, the classification algorithm is used for disc

, generate different combinations, evaluate combinations, and compare them with other combinations. In this way, the selection of a subset is considered an optimization problem,Main methods: Recursive feature elimination algorithm (recursive feature elimination algorithm). Here are a lot of optimization algorithms can be solved, especially some heuristic optimization algorithms, such as GA,PSO,DE,ABC, see "

25 Java machine learning tools and libraries 1. Weka integrates Machine Learning Algorithms for data mining. These algorithms can be directly applied to a dataset or you can write code to call them. Weka includes a series of tool