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Limdu is a machine-learning framework for Node.js. It supports multi-label classification, online learning, and real-time classification. Therefore, it is especially suited for natural language understanding in dialog systems and chat-bots.Limdu is in an "alpha" state - some parts are working (see this readme), but some parts are missing or not tested. Contributions are welcome.

classifier classification categorization text-classification natural-lanaguage-understanding machine-learning multi-label multilabel multi-class multiclass online-learning naive-bayes winnow perceptron svm linear-svm binary-relevance one-vs-allAiLearning: 机器学习 - MachineLearning - ML、深度学习 - DeepLearning - DL、自然语言处理 NLP

fp-growth apriori mahchine-leaning naivebayes svm adaboost kmeans svd pca logistic regression recommendedsystem sklearn scikit-learn nlp deeplearning dnn lstm rnnThis chapter intends to introduce the main objects and concepts in TensorFlow. We also introduce how to access the data for the rest of the book and provide additional resources for learning about TensorFlow. After we have established the basic objects and methods in TensorFlow, we now want to establish the components that make up TensorFlow algorithms. We start by introducing computational graphs, and then move to loss functions and back propagation. We end with creating a simple classifier and then show an example of evaluating regression and classification algorithms.

tensorflow tensorflow-cookbook linear-regression neural-network tensorflow-algorithms rnn cnn svm nlp packtpub machine-learning tensorboard classification regression kmeans-clustering genetic-algorithm odePython codes for common Machine Learning Algorithms

linear-regression polynomial-regression logistic-regression decision-trees random-forest svm svr knn-classification naive-bayes-classifier kmeans-clustering hierarchical-clustering pca lda xgboost-algorithmVehicle detection using these machine learning and computer vision techniques. First, you need to get training data(cars and not-cars). You can get car images from GTI vehicle image database, KITTI vision benchmark). And over 1500 images per each is good for this project.

machine-learning udacity computer-vision svm self-driving-car hog-features sliding-windows svm-classifierMailgun library to extract message quotations and signatures.For machine learning talon currently uses the scikit-learn library to build SVM classifiers. The core of machine learning algorithm lays in talon.signature.learning package. It defines a set of features to apply to a message (featurespace.py), how data sets are built (dataset.py), classifier’s interface (classifier.py).

mail-parser text-extraction svmsvmjs is a lightweight implementation of the SMO algorithm to train a binary Support Vector Machine. As this uses the dual formulation, it also supports arbitrary kernels. Correctness test, together with MATLAB reference code are in /test. Corresponding code is inside /demo directory.

support-vector-machines machine-learning classifier svmJSAT is a library for quickly getting started with Machine Learning problems. It is developed in my free time, and made available for use under the GPL 3. Part of the library is for self education, as such - all code is self contained. JSAT has no external dependencies, and is pure Java. I also aim to make the library suitably fast for small to medium size problems. As such, much of the code supports parallel execution.If you want to use the bleeding edge, but don't want to bother building yourself, I recomend you look at jitpack.io. It can build a POM repo for you for any specific commit version. Click on "Commits" in the link and then click "get it" for the commit version you want.

machine-learning machine-learning-library machine-learning-algorithms svm tsne jsatI just built out v2 of this project that now gives you analytics info from your models, and is production-ready. machineJS is an amazing research project that clearly proved there's a hunger for automated machine learning. auto_ml tackles this exact same goal, but with more features, cleaner code, and the ability to be copy/pasted into production.

machine-learning data-science machine-learning-library machine-learning-algorithms ml data-scientists javascript-library scikit-learn kaggle numerai automated-machine-learning automl auto-ml neuralnet neural-network algorithms random-forest svm naive-bayes bagging optimization brainjs date-night sklearn ensemble data-formatting js xgboost scikit-neuralnetwork knn k-nearest-neighbors gridsearch gridsearchcv grid-search randomizedsearchcv preprocessing data-formatter kaggle-competitionThe mission of ThunderSVM is to help users easily and efficiently apply SVMs to solve problems. ThunderSVM exploits GPUs and multi-core CPUs to achieve high efficiency. It supports all functionalities of LibSVM such as one-class SVMs, SVC, SVR and probabilistic SVMs. It can use same command line options as LibSVM. It supports Python, R and Matlab interfaces.

svm classification regression one-class-learning parallelism cuda support-vector-machineThis package provides a Ruby bindings to the LIBSVM library. SVM is a machine learning and classification algorithm, and LIBSVM is a popular free implementation of it, written by Chih-Chung Chang and Chih-Jen Lin, of National Taiwan University, Taipei. See the book "Programming Collective Intelligence," among others, for a usage example. There is a JRuby implementation of this gem named jrb-libsvm by Andreas Eger.

libsvm ruby-bindings ruby-language-bindings machine-learning svm svm-classifier svm-training svm-learning ml rubymlSupport Vector Machine (SVM) library for nodejs & io.js . Support vector machines are supervised learning models that analyze data and recognize patterns. A special property is that they simultaneously minimize the empirical classification error and maximize the geometric margin; hence they are also known as maximum margin classifiers.

svm machine-learning libsvmSpeech emotion recognition implemented in Keras (LSTM, CNN, SVM, MLP) | 语音情感识别

svm cnn lstm mlp opensmile speech-emotion-recognitiongolinear is a package for training and using linear classifiers in the Go programming language (golang).Ubuntu and Debian provide packages for liblinear. However, at the time of writing (July 2, 2014), these were serverly outdated. This package requires version 1.9 or later.

svm classifier linear-models liblinear machine-learning go-libraryThis is a Golang wrapper for LIBLINEAR (C.-J. Lin et al.) (GitHub). Note that the interface of this package might be slightly different from liblinear C interface because of Go convention. Yet, I'll try to align the function name and functionality to liblinear C library.GoDoc: Document.

machine-learning svmTraining AI machine learning models on the Fashion MNIST dataset. Fashion-MNIST is a dataset consisting of 70,000 images (60k training and 10k test) of clothing objects, such as shirts, pants, shoes, and more. Each example is a 28x28 grayscale image, associated with a label from 10 classes. The 10 classes are listed below.

mnist fashion dataset fashion-mnist machine-learning artificial-intelligence artificial-neural-networks support-vector-machines svm xgboost data-science r supervised-learning classification image-recognition image-classificationAlgorithm::LibLinear - A Perl binding for LIBLINEAR, a library for classification/regression using linear SVM and logistic regression. Algorithm::LibLinear is an XS module that provides features of LIBLINEAR, a fast C library for classification and regression.

svm machine-learning classification regression liblinearSimpleSvm is a minimalistic educational hypervisor for Windows on AMD processors. It aims to provide small and explanational code to use Secure Virtual Machine (SVM), the AMD version of Intel VT-x, with Nested Page Tables (NPT) from a windows driver. SimpleSvm is inspired by SimpleVisor, an Intel x64/EM64T VT-x specific hypervisor for Windows, written by Alex Ionescu (@aionescu).

amd svm hypervisor windows-kernel driver virtual-machineSimpleSvmHook is a research purpose hypervisor for Windows on AMD processors. It hooks kernel mode functions and protects them from being detected using Nested Page Tables (NPT), part of AMD Virtualization (AMD-V) technology. This project is meant to serve as an example implementation of virtual machine introspection (VMI) on AMD processors and highlight differences from similar VMI implementations on Intel processors.

amd svm hypervisor windows-kernel driver virtual-machineThe GDLibrary is a pure-Matlab library of a collection of unconstrained optimization algorithms. This solves an unconstrained minimization problem of the form, min f(x). Note that the SGDLibrary internally contains this GDLibrary.

optimization optimization-algorithms machine-learning machine-learning-algorithms big-data gradient-descent gradient logistic-regression newton linear-regression svm lasso matrix-completion rosenbrock-problem softmax-regression multinomial-regression statistical-learning classification
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