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| + | ====== Pattern recognition algorithms used in data science ====== | ||
| + | * K-Nearest Neighbors (KNN) | ||
| + | * Linear Discriminant Analysis (LDA) | ||
| + | * Quadratic Discriminant Analysis (QDA) | ||
| + | * Decision Trees | ||
| + | * Random Forest | ||
| + | * Naive Bayes | ||
| + | * Support Vector Machines (SVMs) | ||
| + | * Neural Networks (including Deep Learning) | ||
| + | * k-means | ||
| + | * Hierarchical clustering | ||
| + | * DBSCAN | ||
| + | * Principal Component Analysis (PCA) | ||
| + | * Independent Component Analysis (ICA) | ||
| + | * Non-Negative Matrix Factorization (NMF) | ||
| + | * Singular Value Decomposition (SVD) | ||
| + | |||
| + | ===== With time series data, some common pattern recognition algorithms include ===== | ||
| + | |||
| + | * Time Series Decomposition: | ||
| + | * Exponential smoothing: used for forecasting and estimating the trend in time series data | ||
| + | * ARIMA: a class of statistical models for analyzing and forecasting time series data | ||
| + | * Seasonal decomposition of time series by Loess (STL): decompose time series into seasonal, trend, and residual components | ||
| + | * Dynamic Time Warping (DTW): a technique for measuring similarity between two temporal sequences, often used in time series classification | ||
| + | * Hidden Markov Models (HMM): used for modeling sequential data, such as stock prices or speech signals. | ||
| + | * Additionally, | ||
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