In 1986, three scientists improved backpropagation, making neural networks learn effectively from data

A groundbreaking 1986 technique called backpropagation revolutionized artificial intelligence, enabling computers to learn ...
IEEE

An efficient forward-backward algorithm for an explicit-duration hidden Markov model

Abstract: Existing algorithms for estimating the model parameters of an explicit-duration hidden Markov model (HMM) usually require computations as large as O((MD/sup 2/ + M/sup 2/)T) or O(M/sup 2/ DT ...
IEEE

Basmag: An Optimized HMM-Based Localization System Using Backward Sequences Matching Algorithm Exploiting Geomagnetic Information

Abstract: Due to the complex building construction materials, the geomagnetic field has great anomalies in indoor environments. These anomalies generate distinctive signatures corresponding to ...
GitHub

duster-amigos/hmm_cpp

A complete C++ implementation of the Python hmmlearn library, featuring modern C++17, Eigen for linear algebra, and comprehensive HMM algorithms. hmm_c++/ ├── include/ # Header files │ ├── types.hpp # ...
C&EN

Transition Time Determination of Single-Molecule FRET Trajectories via Wasserstein Distance Analysis in Steady-State Variations in smFRET (WAVE)

State Key Laboratory of Surface Physics, Multiscale Research Institute of Complex Systems, Department of Physics, Fudan University, Shanghai 200433, China ...
C&EN

Linear Chain Conditional Random Field for Operating Mode Identification and Multimode Process Monitoring

Creative Commons (CC): This is a Creative Commons license. Attribution (BY): Credit must be given to the creator. As a supervised machine learning algorithm, conditional random fields are mainly used ...
GitHub

Scikit-Learn compatible HMM and DTW based sequence machine learning algorithms in Python.

Sequentia is a Python package that provides various classification and regression algorithms for sequential data, including methods based on hidden Markov models and dynamic time warping. Some ...