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bayesnaive bayes in php
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chattoChatto is a minimal chatbot framework in Go.
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Awesome Decision Tree PapersA collection of research papers on decision, classification and regression trees with implementations.
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Digit Recognizer A Machine Learning classifier for recognizing the digits for humans.
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Whatlang RsNatural language detection library for Rust. Try demo online: https://www.greyblake.com/whatlang/
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Monkeylearn⛔️ ARCHIVED ⛔️ 🐒 R package for text analysis with Monkeylearn 🐒
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EdgemlThis repository provides code for machine learning algorithms for edge devices developed at Microsoft Research India.
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Speech signal processing and classificationFront-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].
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Ml Classify Text JsMachine learning based text classification in JavaScript using n-grams and cosine similarity
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labelReaderProgrammatically find and read labels using Machine Learning
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golinearliblinear bindings for Go
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Naivebayes📊 Naive Bayes classifier for JavaScript
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name2genderExtrapolate gender from first names using Naïve-Bayes and PyTorch Char-RNN
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SytoraA sophisticated smart symptom search engine
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Lclicensechecker (lc) a command line application which scans directories and identifies what software license things are under producing reports as either SPDX, CSV, JSON, XLSX or CLI Tabular output. Dual-licensed under MIT or the UNLICENSE.
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dl-reluDeep Learning using Rectified Linear Units (ReLU)
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