classifier

An NLP library for building bots, with entity extraction, sentiment analysis, automatic language identify, and so more

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

A curated list of data mining papers about fraud detection.

This repository provides code for machine learning algorithms for edge devices developed at Microsoft Research India.

A Python interface for Facebook fastText

A curated list of gradient boosting research papers with implementations.

Natural language detection library for Rust. Try demo online: https://whatlang.org/

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

Real-Time Spatio-Temporally Localized Activity Detection by Tracking Body Keypoints

Machine Learning inference engine for Microcontrollers and Embedded devices

ncnn example: mask detection: anticonv face detection: retinaface&&mtcnn&&centerface, track: iou tracking, landmark: zqcnn, recognize: mobilefacenet classifier: mobilenet object detecter: mobilenetssd

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

A Naive Bayes machine learning implementation in Elixir.

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

A .NET image and video classifier used to identify explicit/pornographic content written in C#.

A License Classifier

A pytorch implemented classifier for Multiple-Label classification

A java classifier based on the naive Bayes approach complete with Maven support and a runnable example.

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

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].