machine learning techniques and algorithms

Tags: dimens span form body share picture dual nbsp COM logLecture 2. Dual support Vector Machine2.1 motivation of Dual suppor vector machineThe linear support vector machine plus feature transformation will be able to get nonlinear supported vector machine. To do so, we can take advantage of the excellent features of the SVM and feature transformation Q1: The smaller VC Dimension (SVM), the complex boundar

optimization techniques such as stochastic gradient descent and semi-positive definite planning has also facilitated the processing of large-scale data and the solution of global optimization. It is worth mentioning that in recent years, depth (neural network) learning through the progressive reduction of feature extraction technology and big data training strategy, in many layers of image, voice and even

intention. Look at the judging criteria below. Using p to express precision,r expression recall; If we choose the criterion = (p+r)/2, then algorithm3 win, obviously unreasonable. Here we introduce an evaluation standard: F1-score. When p = or r=0, there is f=0; When P=1r=1, there is f=1, the largest; Similarly, we apply F1 score to the above three algorithms, and the results are ALGORITHM1 largest, which is the best; algorithm3 the least, the worst

between machine learning and databases, which mainly uses the technology provided by the machine learning community to analyze massive amounts of data and use the technology provided by the database community to manage massive amounts of data.In a word, machine

neural networks and machine learning frameworks, including classes used to create multiple networks, and classes that support the need for data collation and processing in neural networks. Its training uses multi-threaded elastic propagation. It can also use the GPU to speed up processing time. Provides a graphical interface to help model and train neural networks. Neural network designer-This is a dat

implementation.I explain this process as machine learning equals Matrix + statistics + optimization + algorithm . First, when the data is defined as an abstract representation, it often forms a matrix or a graph, which can be understood as a matrix. Statistics is the main tool and way of modeling, and the model solving is mostly defined as an optimization problem, especially, the frequency statistic method

a machine learning course at Stanford University. Take more course notes, complete course assignments as much as possible, and ask more questions. Read some books: This refers not to textbooks, but to the books listed above for beginners of programmers. Master a tool: Learn to use an analysis tool or class library, such as the python Machine

regression, CART, naive Bayes, KNN3 Unsupervised learning techniques--apriori, K-means, PCA2 kinds of integrated learning techniques-random forest bagging, AdaboostWe introduce the top ten machine learning (ML)

Objective Machine learning is divided into: supervised learning, unsupervised learning, semi-supervised learning (can also be used Hinton said reinforcement learning) and so on. Here, the main understanding of supervision and unsu

model and re-experiment to optimize them. (ii) Criteria for numerical evaluation of machine learning algorithms 1. Cross-validation set error (accuracy) This is a good idea, the design of the fitting function if the cross-validation set test error is very large, then certainly not a good learning algorithm; However,

clustering. Select the appropriate machine learning task for a potential application. Apply regression, classification, clustering, retrieval, recommender systems, and deep learning. Represent your data as features to serve as input to machine learning models.

Dr. Hangyuan Li's "Talking about my understanding of machine learning" machine learning and natural language processing [Date: 2015-01-14] Source: Sina Weibo Hangyuan Li [Font: Big Small] Calculating time, from the beginning to the present, do m