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• Relational data mining
  ...ion+retrieval/book/978-3-540-42289-1]</ref> Unlike traditional data mining algorithms, which look for relational data mining algorithms look for patterns among multiple tables
  3 KB (394 words) - 00:03, 15 January 2024
• Amortization
  * [[Amortized analysis]], a method of analysing execution cost of algorithms ..., the time period a non-conforming property has to conform to a new zoning classification before the non-conforming use becomes prohibited
  696 bytes (92 words) - 18:40, 26 July 2024
• Soft independent modelling of class analogies
  ...y samples as belonging to multiple classes and not necessarily producing a classification of samples into non-overlapping classes. In order to build the classification models, the samples belonging to each class need to be analysed using [[pri
  3 KB (431 words) - 21:40, 4 September 2022
• Feature (machine learning)
  ...orithm]]s for [[pattern recognition]], [[Classification (machine learning)|classification]], and [[Regression analysis|regression]] tasks. Features are usually numer ...ht, weight, and income. Numerical features can be used in machine learning algorithms directly.{{cn|date=June 2024}}
  9 KB (1,190 words) - 23:07, 23 May 2025
• PCF
  ...mentation of SNP-array data with two measurement tracks (LRR and BAF). All algorithms are freely available in the R package ''Copynumber''; see [http://www.biome * A US Navy hull classification symbol: [[List of patrol vessels of the United States Navy#Patrol craft fas
  3 KB (412 words) - 23:12, 27 September 2024
• Statistical classification
  When [[classification]] is performed by a computer, statistical methods are normally used to deve ...to the mathematical [[function (mathematics)|function]], implemented by a classification algorithm, that maps input data to a category.
  13 KB (1,772 words) - 17:53, 15 July 2024
• Linear classifier
  {{short description|Statistical classification in machine learning}} ...s]]. Such classifiers work well for practical problems such as [[document classification]], and more generally for problems with many variables ([[feature vector|fe
  9 KB (1,319 words) - 02:44, 21 October 2024
• Outline of ants
  === Biological classification === {{further|Biological classification}}
  3 KB (330 words) - 10:41, 21 June 2024
• Outline of computer science
  ...subject classification system for computer science is the [[ACM Computing Classification System]] devised by the [[Association for Computing Machinery]]. * [[Graph theory]] – Foundations for data structures and searching algorithms.
  11 KB (1,250 words) - 01:41, 19 October 2024
• Learning vector quantization
  ...pe-based]] [[supervised learning|supervised]] [[Statistical classification|classification]] [[algorithm]]. LVQ is the supervised counterpart of [[vector quantization ...ned in the [[feature space]] of observed data. In winner-take-all training algorithms one determines, for each data point, the prototype which is closest to the
  7 KB (1,152 words) - 05:37, 28 May 2025
• Sorting
  ...a common operation in many applications, and efficient [[Sorting algorithm|algorithms]] have been developed to perform it. ====Common algorithms====
  6 KB (972 words) - 16:31, 19 May 2024
• Transduction (machine learning)
  classification, where the inputs tend to cluster in two groups. A large set of about the classification labels. The same predictions would not be obtainable
  11 KB (1,571 words) - 17:59, 25 May 2025
• String-searching algorithm
  ...uiring time proportional to ''N''. This may significantly slow some search algorithms. One of many possible solutions is to search for the sequence of code units This article mainly discusses algorithms for the simpler kinds of string searching.
  18 KB (2,570 words) - 20:41, 23 April 2025
• List of theorems called fundamental
  ...n the field. For instance, the [[fundamental theorem of curves]] describes classification of [[Regular curve|regular curves]] in space up to [[Translation (geometry)
  5 KB (578 words) - 13:53, 14 September 2024
• Instantaneously trained neural networks
  ...S. [https://www.sciencedirect.com/science/article/pii/0167865594900620 New algorithms for training feedforward neural networks]. Pattern Recognition Letters 15: ...for [[deep learning]] and [[data mining]].<ref>Zhang, Z. et al., Document Classification Via TextCC Based on Stereographic Projection and for deep learning, Interna
  5 KB (690 words) - 20:39, 23 March 2023
• Empirical risk minimization
  ...rical risk minimization''' defines a family of [[machine learning|learning algorithms]] based on evaluating performance over a known and fixed dataset. The core ...hat{y}</math> of a hypothesis is from the true outcome <math>y</math>. For classification tasks, these loss functions can be [[scoring rule]]s.
  11 KB (1,652 words) - 10:36, 25 May 2025
• List of graph theory topics
  ==Algorithms== {{See also|Graph algorithms|Algorithm}}
  7 KB (788 words) - 02:52, 24 September 2024
• Selection sort
  ...ed for its simplicity and has performance advantages over more complicated algorithms in certain situations, particularly where [[auxiliary memory]] is limited. Selection sort is not difficult to analyze compared to other sorting algorithms, since none of the loops depend on the data in the array. Selecting the min
  12 KB (1,810 words) - 11:10, 21 May 2025
• Boosting (machine learning)
  ...age=23 |isbn=978-1439830031 |quote=The term boosting refers to a family of algorithms that are able to convert weak learners to strong learners }}</ref> ...learner is a classifier that is arbitrarily well-correlated with the true classification. [[Robert Schapire]] answered the question in the affirmative in a paper pu
  21 KB (2,941 words) - 09:16, 15 May 2025
• Supervised learning
  ...learning algorithm on the gathered training set. Some supervised learning algorithms require the user to determine certain [[Hyperparameter (machine learning)|c A wide range of supervised learning algorithms are available, each with its strengths and weaknesses. There is no single l
  22 KB (3,185 words) - 13:51, 28 March 2025