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Everything about Statistical classification
Categorization of data using statistics
When classification is performed by a computer, statistical methods are normally used to develop the algorithm. Wikipedia →
🧭 Start here: a 6-step reading path
- Read the overview: Statistical classificationA 5-minute orientation
- Skim a recent paper: Fusing CNNs and statistical indicators to improve image classificationInfluential work since 2022
- Pick up a classic book: 1980 census of populationThe most-reprinted book on the subject
- Read the landmark paper: Traffic classification through simple statistical fingerprintingCited 320 times
- Go sideways: Support vector machineA closely related topic
- Go sideways: Outline of machine learningA closely related topic
📖 Definitions
No dictionary entry for this exact phrase.
🏆 Landmark papers (most cited)
Traffic classification through simple statistical fingerprinting
Statistical Normalization and Back Propagationfor Classification
Signal classification using statistical moments
Map-Pattern Classification by Statistical Methods
Statistical geometrical features for texture classification
Statistical models for slope instability classification
Bug Distribution and Statistical Pattern Classification
Unifying statistical texture classification frameworks
🆕 Influential since 2022
Fusing CNNs and statistical indicators to improve image classification
Drone Detection and Classification Using Physical-Layer Protocol Statistical Fingerprint
Classification of flotation frothers – A statistical approach
Precise statistical analysis of classification accuracies for adversarial training
Machine learning and statistical classification in CRISPR-Cas12a diagnostic assays
📚 Books worth reading
- 📕1980 census of populationUnited States. Bureau of the Census. Population Division · 1981
PopulationUnited States. Bureau of the Census · 1943
Manual of the international statistical classification of diseases, injuries, and causes of deathWorld Health Organization (WHO) · 1948
U.S. foreign trade statisticsUnited States. Bureau of the Census · 1967
Cluster randomised trialsHayes, Richard J. DSc., Lawrence H. Moulton · 2008
Disorders of personalityTheodore Millon · 1981
Topics in modelling of clustered dataMarc Aerts · 2002- 📕Occupational structures of industriesManuel Zymelman · 1980
Ranked by number of editions on Open Library, a rough measure of how lasting a book is.
🔗 Related topics
- Support vector machine In machine learning, a support vector machine (SVM) or support vector network is a supervised max-margin model with associated learning algorithms that…
- 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…
- Pattern recognition Pattern recognition is the task of assigning a class to an observation based on patterns extracted from data. While similar, pattern recognition (PR) is…
- 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…
- List of things named after Thomas Bayes Thomas Bayes (/beɪz/ BAYZ; c. 1701 – 1761) was an English statistician, philosopher, and Presbyterian minister. Bayesian (/ˈbeɪziən/ BAY-zee-ən or /ˈbeɪʒən/…
- 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…
- Calibration (statistics) There are two main uses of the term calibration in statistics that denote special types of statistical inference problems. Calibration can mean a reverse…
- Hinge loss In machine learning, hinge loss is a loss function used for training classifiers. Hinge loss is used for "maximum-margin" classification, most notably…