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Everything about Probabilistic classification
Machine learning problem
In machine learning, a probabilistic classifier is a classifier that is able to predict, given an observation of an input, a probability distribution over a set of classes, rather than only outputting the most likely class that the observation should belong to. Probabilistic classifiers provide classification that can be useful in its own right or when combining classifiers into ensembles. Wikipedia →
🧭 Start here: a 6-step reading path
- Read the overview: Probabilistic classificationA 5-minute orientation
- Skim a recent paper: Semisupervised Hyperspectral Image Classification Using a Probabilistic Pseudo-Label Generation FrameworkInfluential work since 2022
- Pick up a classic book: Dynamical systems and group actionsThe most-reprinted book on the subject
- Read the landmark paper: Probabilistic classification learning in amnesia.Cited 444 times
- Go sideways: Calibration (statistics)A closely related topic
- Go sideways: Platt scalingA closely related topic
📖 Definitions
No dictionary entry for this exact phrase.
🏆 Landmark papers (most cited)
Probabilistic classification learning in amnesia.
Probabilistic Posture Classification for Human-Behavior Analysis
Probabilistic neural network for breast cancer classification
Visual Methods for Analyzing Probabilistic Classification Data
Unbiased probabilistic taxonomic classification for DNA barcoding
Probabilistic Classification Vector Machines
Probabilistic classification learning in Tourette syndrome
CPT-Based Probabilistic Soil Characterization and Classification
🆕 Influential since 2022
Semisupervised Hyperspectral Image Classification Using a Probabilistic Pseudo-Label Generation Framework
Predictive Multiplicity in Probabilistic Classification
Multiscale Convolution-Based Probabilistic Classification for Detecting Bare PCB Defects
Brain tumor segmentation and classification with hybrid clustering, probabilistic neural networks
Probabilistic deep metric learning for hyperspectral image classification
📚 Books worth reading
- 📕Dynamical systems and group actionsLewis Bowen, R. I. Grigorchuk · 2012
Algebraic And Geometric Aspects Of Integrable Systems And Random Matrices Ams Special Session Algebraic And Geometric Aspects Of Integrable Systems And Random Matrices January 67 2012 Boston MaAlgebraic and · 2013
Ranked by number of editions on Open Library, a rough measure of how lasting a book is.
🔗 Related topics
- 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…
- 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…
- Statistical classification When classification is performed by a computer, statistical methods are normally used to develop the algorithm. Often, the individual observations are…
- Naive Bayes classifier In statistics, naive (sometimes simple or idiot's) Bayes classifiers are a family of "probabilistic classifiers" which assume that the features are conditionall…
- Discriminative model Discriminative models, also referred to as conditional models, are a class of models frequently used for classification. In machine learning, it typically…
- Bayes error rate In statistical classification, Bayes error rate is the lowest possible error rate for any classifier of a random outcome (into, for example, one of two…
- Generative model Generative models are a class of computational models frequently used for classification. In machine learning, it typically models the joint distribution…
- Multiclass classification In machine learning and statistical classification, multiclass classification or multinomial classification is the problem of classifying instances into…