Everything about Pattern recognition
Automated recognition of patterns and regularities in data
Pattern recognition is the task of assigning a class to an observation based on patterns extracted from data. While similar, pattern recognition (PR) is not to be confused with pattern machines (PM) which may possess PR capabilities but their primary function is to distinguish and create emergent patterns. PR has applications in statistical data analysis, signal processing, image analysis, information retrieval, bioinformatics, data compression, computer graphics and machine learning. Pattern recognition has its origins in statistics and engineering; some modern approaches to pattern recognition include the use of machine learning, due to the increased availability of big data and a new abundance of processing power. Wikipedia →
🧭 Start here: a 6-step reading path
- Read the overview: Pattern recognitionA 5-minute orientation
- Skim a recent paper: Pattern recognition receptors: function, regulation and therapeutic potentialInfluential work since 2022
- Pick up a classic book: Artificial Intelligence Applications and InnovationsThe most-reprinted book on the subject
- Read the landmark paper: Dictionary of protein secondary structure: Pattern recognition of hydrogen‐bonded and geometrical featuresCited 14,016 times
- Go sideways: Statistical classificationA closely related topic
- Go sideways: Supervised learningA closely related topic
📖 Definitions
Noun The detection of patterns and regularities in raw data.
🗂️ The subject at a glance
🏆 Landmark papers (most cited)
Dictionary of protein secondary structure: Pattern recognition of hydrogen‐bonded and geometrical features
A Tutorial on Support Vector Machines for Pattern Recognition
The role of pattern-recognition receptors in innate immunity: update on Toll-like receptors
Pattern Recognition Receptors and Inflammation
Visual pattern recognition by moment invariants
Statistical pattern recognition: a review
Neocognitron: A self-organizing neural network model for a mechanism of pattern recognition unaffected by shift in position
Face Description with Local Binary Patterns: Application to Face Recognition
🆕 Influential since 2022
Pattern recognition receptors: function, regulation and therapeutic potential
Battery health estimation with degradation pattern recognition and transfer learning
Quantum-Enhanced Pattern Recognition
Tourism demand forecasting using compound pattern recognition
Machine Learning in Pattern Recognition
📚 Books worth reading
Artificial Intelligence Applications and InnovationsLazaros S. Iliadis, Ilias Maglogiannis · 2006
Artificial Intelligence and Soft ComputingLeszek Rutkowski · 2012
Intelligent Robotics and ApplicationsHonghai Liu · 2010
Pattern RecognitionCheng-Lin Liu · 2012
Neural Information ProcessingLong Cheng, Andrew Chi Sing Leung · 2018
Advances in visual computingGeorge Bebis, Richard Boyle · 2005
Neural Information ProcessingTingwen Huang · 2012
Image Analysis and RecognitionAurélio Campilho · 2004
Ranked by number of editions on Open Library, a rough measure of how lasting a book is.
🔗 Related topics
- Statistical classification When classification is performed by a computer, statistical methods are normally used to develop the algorithm. Often, the individual observations are…
- Supervised learning In machine learning, supervised learning (SL) is a type of machine learning paradigm where an algorithm learns to map input data to a specific output based…
- Outline of machine learning The following outline is provided as an overview of, and topical guide to, machine learning: Machine learning (ML) is a subfield of artificial intelligence…
- Generative model Generative models are a class of computational models frequently used for classification. In machine learning, it typically models the joint distribution…
- Linear classifier In machine learning, a linear classifier makes a classification decision for each object based on a linear combination of its features. A simpler definition…
- Probabilistic classification In machine learning, a probabilistic classifier is a classifier that is able to predict, given an observation of an input, a probability distribution over…
- Platt scaling In machine learning, Platt scaling or Platt calibration is a way of transforming the outputs of a classification model into a probability distribution…
- Conditional random field In pattern recognition and machine learning, conditional random field (CRF) is a class of statistical modeling methods often used for structured prediction…