| Classification method | See ▶ pattern recognition |
| Deep learning | ▶ Machine learning method for deep neural networks |
| Deep neural network (DNN) | Artificial neural network with numerous hidden layers ▶ pattern recognizer for ▶ (sequences of) feature vectors |
| EM algorithm | ▶ Machine learning method for ▶ Gaussian mixture models and ▶ hidden Markov models |
| Feature analysis | Method for calculation of ▶ (sequences of) feature vectors from measured signals |
| Feature vector (sequence) | Set of ▶ classification-relevant numerical parameters, possibly as a temporal sequence |
| Gaussian mixture model (GMM) | Statistical ▶ model for ▶ pattern recognition in ▶ feature vectors based on Gaussian mixture distribution densities |
| Hidden Markov model (HMM) | Statistical ▶ model for ▶ pattern recognition in ▶ feature vector sequences based on a Markov process, e.g., GMM-HMM, DNN-HMM |
| Machine learning | Automated method for building of ▶ models for ▶ pattern recognition and decision-making processes |
| Model | Here: computational representation of knowledge |
| Pattern recognition | Method for differentiation into predefined classes, e.g., based on ▶ DNN, ▶ GMM, ▶ HMM, or ▶ SVM |
| Primary analysis | First step in ▶ feature analysis (signal processing, e.g., filter banks, FFT, STFT, DWT, Cepstrum, LPC, Wigner-Ville distribution, etc.) |
| Secondary analysis | Second step in ▶ feature analysis (statistics, data compression, e.g., quantiles, moments, differences, filtering, PCA, LDA, ICA, JFA, etc.) |
| Semantic processing | Computational processing of meanings (e.g., of measured signals) |
| Sequence classifier | ▶ Pattern recognizer for a sequence of ▶ feature vectors |
| Signal analysis | See ▶ primary analysis |
| Support vector machine (SVM) | ▶ Pattern recognizer for ▶ feature vectors |
| Training method | See ▶ machine learning |
| Vector classifier | ▶ Pattern recognizer for a ▶ feature vector |