A collection of research papers on decision, classification and regression trees with implementations.

A curated list of data mining papers about fraud detection.

A curated list of gradient boosting research papers with implementations.

Repository for the paper "Automated Hate Speech Detection and the Problem of Offensive Language", ICWSM 2017

A Naive Bayes machine learning implementation in Elixir.

Natural language detection library for Rust. Try demo online: https://www.greyblake.com/whatlang/

Organize your folders into a beautiful classified folder structure with this perfect tool

custom human activity recognition modules by pose estimation and cascaded inference using sklearn API

This is a pytorch re-implementation of Learning a Discriminative Filter Bank Within a CNN for Fine-Grained Recognition

A pytorch implemented classifier for Multiple-Label classification

An example of how to use CreateML in Xcode 10 to create a Core ML model for classifying text

yet another general purpose naive bayesian classifier.

ERRor ANnotation Toolkit: Automatically extract and classify grammatical errors in parallel original and corrected sentences.

A License Classifier

Scene text detection and recognition based on Extremal Region(ER)

Front-end speech processing aims at extracting proper features from short- term segments of a speech utterance, known as frames. It is a pre-requisite step toward any pattern recognition problem employing speech or audio (e.g., music). Here, we are interesting in voice disorder classification. That is, to develop two-class classifiers, which can discriminate between utterances of a subject suffering from say vocal fold paralysis and utterances of a healthy subject.The mathematical modeling of the speech production system in humans suggests that an all-pole system function is justified [1-3]. As a consequence, linear prediction coefficients (LPCs) constitute a first choice for modeling the magnitute of the short-term spectrum of speech. LPC-derived cepstral coefficients are guaranteed to discriminate between the system (e.g., vocal tract) contribution and that of the excitation. Taking into account the characteristics of the human ear, the mel-frequency cepstral coefficients (MFCCs) emerged as descriptive features of the speech spectral envelope. Similarly to MFCCs, the perceptual linear prediction coefficients (PLPs) could also be derived. The aforementioned sort of speaking tradi- tional features will be tested against agnostic-features extracted by convolu- tive neural networks (CNNs) (e.g., auto-encoders) [4]. The pattern recognition step will be based on Gaussian Mixture Model based classifiers,K-nearest neighbor classifiers, Bayes classifiers, as well as Deep Neural Networks. The Massachussets Eye and Ear Infirmary Dataset (MEEI-Dataset) [5] will be exploited. At the application level, a library for feature extraction and classification in Python will be developed. Credible publicly available resources will be 1used toward achieving our goal, such as KALDI. Comparisons will be made against [6-8].

A Machine Learning classifier for recognizing the digits for humans.

📊 Naive Bayes classifier for JavaScript

Object classification with CIFAR-10 using transfer learning

Machine Learning inference engine for Microcontrollers and Embedded devices

This case study shows how to create a model for text analysis and classification and deploy it as a web service in Azure cloud in order to automatically classify support tickets. This project is a proof of concept made by Microsoft (Commercial Software Engineering team) in collaboration with Endava http://endava.com/en

A Naive Bayesian Classifier written in Python

The purpose of this tutorial is to learn how to install and prepare TensorFlow framework to train your own convolutional neural network object detection classifier for multiple objects, starting from scratch

🐒 R package for text analysis with Monkeylearn 🐒

Efficient tree-based convolutional neural networks in TensorFlow