The function consist of two parts a simple weak classifier and a boosting part:The weak classifier tries to find the best threshold in one of the data dimensions to separate the data into two classes -1 and 1The boosting part calls the classifier iteratively, after every classification step it changes the weights of miss-classified examples. This creates a cascade of "weak classifiers" which behaves like a "strong classifier".Training mode: [estimateclass,model]=adaboost('train',datafeatures,dataclass,itt)Apply mode: estimateclass=adaboost('apply',datafeatures,model) inputs/outputs: datafeatures : An Array with size number_samples x number_features dataclass : An array with the class off all examples, the class can be -1 or 1 itt : The number of training iterations model : A struct with the cascade of weak-classifiers estimateclass : The by the adaboost model classified data .Please leave a comment, if you like the code, find a bug or have a suggestion.
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Established in 1996, the Journal of Statistical Software publishes articles on statistical software along with the source code of the software itself and replication code for all empirical results. Furthermore, shorter code snippets are published as well as book reviews and software reviews. All contents are freely available online under open licenses. We aim to present research that demonstrates the joint evolution of computational and statistical methods and facilitates their application in practice. Implementations can use languages and environments like R, Python, Julia, MATLAB, SAS, Stata, C, C++, Fortran, among others. See our mission statement for more details.
If the user wants to try this feature with TensorFlow 1.15, they need to download the source code that has been ported and optimized by Intel and compile it (it should be particularly pointed out that Bazel* 0.24.1 needs to be installed for the purpose):
Intel Distribution for Python* is a Python development toolkit for artificial intelligence software developers. It can be used to accelerate computational speed of Python on the Intel Xeon Scalable Processor platform. It is available at Anaconda, and it can also be installed and used with Conda, PIP, APT GET, YUM, Docker, among others. Reference and download site: -for-python.html
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