examples of machine learning projects

Java Virtual machine learning-in-depth understanding of the JVM (1)Java Virtual machine learning-slowly pondering the JVM (2)Java Virtual machine learning-slowly pondering the working mechanism of the JVM (2-1) ClassLoaderJava Vir

Chapter 1 of python Learning (simple examples and common data types) and python Data Types AIYQ195 learn python Chapter 1 simple examples and common data types 1. hello programs required for getting started with programs The program code of the IDE3.4 software is as follows: Print ("hello aiyq195 ") The execution result is as follows: Python 3.4.4 (v3.4.4: 737efc

sophisticated machine learning library, widely used in industry and academia. One thing about Scikit-learn very impressive is that it maintains a very consistent "fit", "predictive" and "test" APIs in many numerical techniques and algorithms, making it very easy to use. In addition to this consistent API design, Scikit-learn also provides some useful tools for dealing with data that is common in many

classifier to classify. The most obtained category is the final category of D .) Boosting: The main feature is Adaboost (Adaptive boosting ). During initialization, an equal weight of 1/N is assigned to each training instance. Then, the learning algorithm is used to train t-round training in the training set. After each training, assign a large weight to the training examples that fail to be trained, that

the process of experience E.For message classification, spam and non-spam classification is the task T, the correct rate of classification is performance p, check whether the mailing label is garbage or non-spam is experience E.For machine learning algorithms can be divided into:-Supervised learning-Non-supervised learningSome

generalization ability, easy to fit. When using discrete features, when a feature becomes multiple and weights become multiple, the influence of successive features on the model is dispersed and weakened, thus reducing the risk of fitting. ) Li Yu once said: whether the model uses discrete or continuous features is actually a trade-off between a "mass discrete feature + simple model" and a "small number of continuous features + complex models". The linear model can be discretized, and the cont

called doget () when it is get, and Dopost () is called when it is post.61. The life cycle of the servletThe Web container loads the servlet, beginning with the life cycle. The servlet is initialized by calling the servlet's init () method. By invoking the service () method implementation, different do*** () methods are called depending on the request. To end the service, the Web container invokes the servlet's Destroy () method.62, how to live servlet single-threaded mode63. How to transfer ob

create a branch for each possible value of the root node property and arrange the training samples under the appropriate branches. Then repeat the entire process, using the training sample associated with each branch node to select the best properties to be tested at that point. This creates a greedy search for a qualifying decision tree (greedy search), which means that the algorithm never re-considers the original selection.specifically for learning