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...(ML), a '''margin classifier''' is a type of [[Statistical classification|classification]] model which is able to give an associated distance from the [[decision bo ...roximation error|error bound]] in [[Boosting (machine learning)|boosting]] algorithms and [[support vector machine]]s is particularly prominent. ...

...date=22 September 2023 |url=https://www.prodigitalweb.com/machine-learning-algorithms-for-beginners/ |access-date=2023-10-01 |language=en-US}}</ref> the LogitBoost algorithm minimizes the [[Loss functions for classification|logistic loss]]: ...

{{Short description|A classification model in machine learning based on centroids}} [[Image:Rocchioclassgraph.jpg|thumb|right|250px|Rocchio Classification]] ...

...nique was later applied to a broad selection of [[Classification algorithm|classification]] and [[Regression analysis|regression]] problems.{{sfn | Kogler | Traxler ...converge to solution very fast.{{sfn | Wang | Han | 2015 | p=11}} For the algorithms that are sublinear in space (e.g., counting the distinct elements of a stre ...

{{Short description|Problem in machine learning and statistical classification}} {{Distinguish|multi-label classification}} ...

==Lance–Williams algorithms== ...algorithms are an infinite family of agglomerative hierarchical clustering algorithms which are represented by a recursive formula for updating cluster distances ...

...mes |author4=Eibe Frank |year=2009|title=Classifier Chains for Multi-label Classification|journal=Proc 13th European Conference on Principles and Practice of Knowled ...thor4=Eyke Hüllermeier |year=2010|title=On label dependence in multi-label classification|journal=Workshop Proceedings of Learning from Multi-Label Data|volume=2010| ...

...tle=Algorithmic Topology and Classification of 3-Manifolds|volume=9|series=Algorithms and Computation in Mathematics|first=Sergei|last=Matveev|publisher=Springer ...

...xity of satisfying finite sets of relations; however, the Max/min CSP/Ones classification theorems give information about the complexity of [[approximation algorithm ...ions below, '''the problem's complexity class is determined by the topmost classification that it satisfies'''. ...

In some classification problems, when random forest is used to fit models, jackknife estimated var ...m|E-mail spam]] problem is a common [[Classification problem (mathematics)|classification problem]], in this problem, 57 features are used to classify spam e-mail an ...

| title = Ultraconservative Online Algorithms for Multiclass Problems ...a [[machine learning]] algorithm, an [[online algorithm]] for [[multiclass classification]] problems. It is designed to learn a set of parameters (vector or matrix) ...

...e estimators''' ('''AODE''') is a probabilistic [[Classifier (mathematics)|classification learning]] technique. It was developed to address the attribute-independen ...ingle class, allowing the user to determine the confidence with which each classification can be made. Its probabilistic model can directly handle situations where s ...

...n=0-914894-95-1|chapter=Ch.9: Algorithms for VLSI Design Tools}} describes algorithms for [[polygon operations]] involved in electronic design automation ([[desi ...118223|isbn=0-8186-2062-5 |s2cid=62735730 }}</ref> In the contexts of many algorithms for largest empty rectangles, "maximal empty rectangles" are candidate sol ...

| title = Efficient statistical classification of satellite measurements ==Use for statistical classification== ...

...[computer vision]], is an approach of [[Classification in machine learning|classification]] based on contextual information in images. "Contextual" means this approa ...ions rather than noise, and in this case the method is actually making the classification worse. ...

...o|year=2006|title=Switching Neural Networks: A new connectionist model for classification|url=https://cdn.rulex.ai/static/Switching_Neural_Networks_A_New_Connectioni ...different sectors, including the field of [[medicine]] (orthopedic patient classification,<ref>{{cite journal ...

...re sequence data are to be [[Cluster analysis|clustered]] or [[statistical classification|classified]], e.g. in [[text mining]] and [[bioinformatics|gene analysis]]. | contribution = The spectrum kernel: A string kernel for SVM protein classification ...

...t-sensitive machine learning tackles is that minimizing different kinds of classification errors is a [[multi-objective optimization]] problem. ...asses, assigning a cost value to each combination. For instance, in binary classification, it may distinguish costs for false positives and false negatives. The util ...

...that involves [[predicting]] structured objects, rather than [[Statistical classification|discrete]] or [[Regression analysis|real]] values.<ref>Gökhan BakIr, Ben Ta ...is problem can be solved by simply performing [[Statistical classification|classification]] of individual [[Lexical analysis#Lexical token and lexical tokenization|t ...

...sionally mislabeled, or the inputs may have some false information, or the classification of the examples may have been maliciously adulterated. ...-MLW-91.pdf An investigation of noise-tolerant relational concept learning algorithms]." Proceedings of the 8th International Workshop on Machine Learning. 1991. ...