Displaying 1 to 6 from 6 results

K-Nearest-Neighbors-with-Dynamic-Time-Warping - Python implementation of KNN and DTW classification algorithm

  •    Jupyter

When it comes to building a classification algorithm, analysts have a broad range of open source options to choose from. However, for time series classification, there are less out-of-the box solutions. I began researching the domain of time series classification and was intrigued by a recommended technique called K Nearest Neighbors and Dynamic Time Warping. A meta analysis completed by Mitsa (2010) suggests that when it comes to timeseries classification, 1 Nearest Neighbor (K=1) and Dynamic Timewarping is very difficult to beat [1].

libtext_bayes - Just another Naive Bayes text classifier library for C++

  •    C++

This is a example how to use Naive Bayes to classify SPAM messages, you can use it for other purposes... Optimizing with inline ASM (I think do it by using SiMD)...

DeepSuperLearner - DeepSuperLearner - Python implementation of the deep ensemble algorithm

  •    Python

This is a sklearn implementation of the machine-learning DeepSuperLearner algorithm, A Deep Ensemble method for Classification Problems. For details about DeepSuperLearner please refer to the https://arxiv.org/abs/1803.02323: Deep Super Learner: A Deep Ensemble for Classification Problems by Steven Young, Tamer Abdou, and Ayse Bener.




Breast-Cancer-Neural-Networks

  •    Matlab

This code helps you classify malignant and benign tumors using Neural Networks. The example code is in Matlab (R2016 or higher will work).

zimbra-ml - Zimbra Machine Learning GraphQL Server

  •    Python

Welcome to the open-source, Zimbra Machine Learning server for text and email classification. This server provides a solution for basic text classification requirements, including smartfolders by topic, sentiment, author, and social toxicity/obscenity, or an unlimited number of other classifications. All of this capability from defining a classifier to loading or creating a new vocabulary, training the classifier, and classifying content can be accessed through the GraphQL API or directly through the libraries. In the current version, classification is performed using a neural network designed by the server's original author and described below. The neural network is a multi-input convolutional graph without pooling that is the same for all types of tasks.