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Bayes is a Naive Bayes Classifier for iOS and Mac platforms. Bayes is implemented in Swift and takes advantage of generics to enable any Hashable, Equatable type of your choosing or creation for use as category or feature.

https://github.com/fcanas/BayesTags | bayes naive-bayes-classifier |

Implementation | Swift |

License | MIT |

Platform | MacOS |

A Naive Bayes machine learning implementation in Elixir. In machine learning, naive Bayes classifiers are a family of simple probabilistic classifiers based on applying Bayes' theorem with strong (naive) independence assumptions between the features.

naive-bayes-classifier bayes machine-learning classifierbayes takes a document (piece of text), and tells you what category that document belongs to. Returns an instance of a Naive-Bayes Classifier.

naive bayes categorize classify classifierThis is a node.js module that classifies if a sentence can be replied with "that's what she said". You change algorithm from the default naive bayes classifier (nbc) to a k-nearest neighbor algorithm (knn).

machine-learning classifier twss aiPOPFile is an email classification tool with a Naive Bayes classifier, POP3, SMTP, NNTP proxies and IMAP filter and a web interface. It runs on most platforms and with most email clients.

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-allphpInsight is a sentiment classifier. It uses a dictionary of words that are categorised as positive, negative or neutral, and a naive bayes algorithm to calculate sentiment. To improve accuracy, phpInsight removes 'noise' words. For example usage, see the examples folder.

Python 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-algorithmRumale (Ruby machine learning) is a machine learning library in Ruby. Rumale provides machine learning algorithms with interfaces similar to Scikit-Learn in Python. Rumale supports Linear / Kernel Support Vector Machine, Logistic Regression, Linear Regression, Ridge, Lasso, Kernel Ridge, Factorization Machine, Naive Bayes, Decision Tree, AdaBoost, Gradient Tree Boosting, Random Forest, Extra-Trees, K-nearest neighbor classifier, K-Means, K-Medoids, Gaussian Mixture Model, DBSCAN, SNN, Power Iteration Clustering, Mutidimensional Scaling, t-SNE, Principal Component Analysis, Kernel PCA and Non-negative Matrix Factorization. This project was formerly known as "SVMKit". If you are using SVMKit, please install Rumale and replace SVMKit constants with Rumale.

machine-learning data-science data-analysis artificial-intelligenceBayes++ is a library of C++ classes that implement numerical algorithms for Bayesian Filtering. They provide tested and consistent numerical methods and the class hierarchy represents the wide variety of Bayesian filtering algorithms and system model

bayes-irc provides a library and plugins for use in IRC Chat. The idea is to bring bayesian filtering known from E-Mail Spamfilters to IRC-Chatclients.

A spell corrector that uses Bayes algorithm and BK (Burkhard-Keller) tree.

bayes db4o spell-checkVIBES stands for Variational Inference in BayES Nets. It consists of a graphical Bayes Net editor and an inference engine which allows variational inference to be applied automatically using Variational Message Passing.

Think Bayes is an introduction to Bayesian statistics using computational methods. This is the repository for the second edition. The premise of this book, and the other books in the Think X series, is that if you know how to program, you can use that skill to learn other topics.

Java port and extension of MLC++ 2.0 by Kohavi et al. Currently contains ID3, C4.5, Naive (aka Simple) Bayes, and FSS and CHC (genetic algorithm) wrappers for feature selection. WEKA 3 interfaces are in development.

Course materials for General Assembly's Data Science course in Washington, DC (8/18/15 - 10/29/15).

data-science machine-learning scikit-learn data-analysis pandas jupyter-notebook course linear-regression logistic-regression model-evaluation naive-bayes natural-language-processing decision-trees ensemble-learning clustering regular-expressions web-scraping data-visualization data-cleaningWe use and compare various different methods for sentiment analysis on tweets (a binary classification problem). The training dataset is expected to be a csv file of type tweet_id,sentiment,tweet where the tweet_id is a unique integer identifying the tweet, sentiment is either 1 (positive) or 0 (negative), and tweet is the tweet enclosed in "". Similarly, the test dataset is a csv file of type tweet_id,tweet. Please note that csv headers are not expected and should be removed from the training and test datasets. There are some general library requirements for the project and some which are specific to individual methods. The general requirements are as follows.

machine-learning deeplearning sentiment-analysis sentiment-classification cnn keras lstmThe Gesture Recognition Toolkit (GRT) is a cross-platform, open-source, C++ machine learning library designed for real-time gesture recognition. Classification: Adaboost, Decision Tree, Dynamic Time Warping, Gaussian Mixture Models, Hidden Markov Models, k-nearest neighbor, Naive Bayes, Random Forests, Support Vector Machine, Softmax, and more...

gesture-recognition grt machine-learning gesture-recognition-toolkit support-vector-machine random-forest kmeans dynamic-time-warping softmax linear-regressionPractice and tutorial-style notebooks covering wide variety of machine learning techniques

numpy statistics pandas matplotlib regression scikit-learn classification principal-component-analysis clustering decision-trees random-forest dimensionality-reduction neural-network deep-learning artificial-intelligence data-science machine-learning k-nearest-neighbours naive-bayesI 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 library's full documentation can be found here. Be sure to lint & pass the unit tests before submitting your pull request.

natural-language-processing machine-learning fuzzy-matching clustering record-linkage bayes bloom-filter canberra caverphone chebyshev cologne cosine classifier daitch-mokotoff dice fingerprint fuzzy hamming k-means jaccard jaro lancaster levenshtein lig metaphone mra ngrams nlp nysiis perceptron phonetic porter punkt schinke sorensen soundex stats tfidf tokenizer tversky vectorizer winkler
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