nz.ac.waikato.cms.weka » kfPMMLClassifierScoring
A Knowledge Flow plugin that provides a Knowledge Flow step for scoring test sets or instance streams using a PMML classifier.
更新时间: 1970-01-01 08:00nz.ac.waikato.cms.weka » lazyBayesianRules
Lazy Bayesian Rules Classifier. The naive Bayesian classifier provides a simple and effective approach to classifier learning, but its attribute independence assumption is often violated in the real world. Lazy Bayesian Rules selectively relaxes the independence assumption, achieving lower error rates over a range of learning tasks. LBR defers processing to classification time, making it a highly efficient and accurate classification algorithm when small numbers of objects are to be classified. For more information, see: Zijian Zheng, G. Webb (2000). Lazy Learning of Bayesian Rules. Machine Learning. 4(1):53-84.
更新时间: 1970-01-01 08:00nz.ac.waikato.cms.weka » leastMedSquared
Implements a least median squared linear regression utilizing the existing weka LinearRegression class to form predictions. Least squared regression functions are generated from random subsamples of the data. The least squared regression with the lowest meadian squared error is chosen as the final model. The basis of the algorithm is Peter J. Rousseeuw, Annick M. Leroy (1987). Robust regression and outlier detection.
更新时间: 1970-01-01 08:00nz.ac.waikato.cms.weka » levenshteinEditDistance
Computes the Levenshtein edit distance between two strings.
更新时间: 1970-01-01 08:00nz.ac.waikato.cms.weka » LibLINEAR
A wrapper class for the liblinear tools (the liblinear classes, typically the jar file, need to be in the classpath to use this classifier). Rong-En Fan, Kai-Wei Chang, Cho-Jui Hsieh, Xiang-Rui Wang, Chih-Jen Lin (2008). LIBLINEAR - A Library for Large Linear Classification.
更新时间: 1970-01-01 08:00nz.ac.waikato.cms.weka » linearForwardSelection
Extension of BestFirst. Takes a restricted number of k attributes into account. Fixed-set selects a fixed number k of attributes, whereas k is increased in each step when fixed-width is selected. The search uses either the initial ordering to select the top k attributes, or performs a ranking (with the same evalutator the search uses later on). The search direction can be forward, or floating forward selection (with opitional backward search steps). For more information see: Martin Guetlein (2006). Large Scale Attribute Selection Using Wrappers. Freiburg, Germany.
更新时间: 1970-01-01 08:00nz.ac.waikato.cms.weka » multiBoostAB
Class for boosting a classifier using the MultiBoosting method. MultiBoosting is an extension to the highly successful AdaBoost technique for forming decision committees. MultiBoosting can be viewed as combining AdaBoost with wagging. It is able to harness both AdaBoost's high bias and variance reduction with wagging's superior variance reduction. Using C4.5 as the base learning algorithm, Multi-boosting is demonstrated to produce decision committees with lower error than either AdaBoost or wagging significantly more often than the reverse over a large representative cross-section of UCI data sets. It offers the further advantage over AdaBoost of suiting parallel execution. For more information, see Geoffrey I. Webb (2000). MultiBoosting: A Technique for Combining Boosting and Waggin
更新时间: 1970-01-01 08:00nz.ac.waikato.cms.weka » multilayerPerceptronCS
An extension of the standard MultilayerPerceptron classifier in Weka that adds context-sensitive Multiple Task Learning (csMTL)
更新时间: 1970-01-01 08:00nz.ac.waikato.cms.weka » naiveBayesTree
Class for generating a decision tree with naive Bayes classifiers at the leaves. For more information, see Ron Kohavi: Scaling Up the Accuracy of Naive-Bayes Classifiers: A Decision-Tree Hybrid. In: Second International Conference on Knoledge Discovery and Data Mining, 202-207, 1996.
更新时间: 1970-01-01 08:00Nearest-neighbor-like algorithm using non-nested generalized exemplars (which are hyperrectangles that can be viewed as if-then rules). For more information, see Brent Martin (1995). Instance-Based learning: Nearest Neighbor With Generalization. Hamilton, New Zealand. Sylvain Roy (2002). Nearest Neighbor With Generalization. Christchurch, New Zealand.
更新时间: 1970-01-01 08:00nz.ac.waikato.cms.weka » normalize
An instance filter that normalize instances considering only numeric attributes and ignoring class index
更新时间: 1970-01-01 08:00nz.ac.waikato.cms.weka » ordinalLearningMethod
An implementation of the Ordinal Learning Method (OLM). Further information regarding the algorithm and variants can be found in: Arie Ben-David (1992). Automatic Generation of Symbolic Multiattribute Ordinal Knowledge-Based DSSs: methodology and Applications. Decision Sciences. 23:1357-1372.
更新时间: 1970-01-01 08:00nz.ac.waikato.cms.weka » ordinalStochasticDominance
An implementation of the Ordinal Stochastic Dominance Learner. Further information regarding the OSDL-algorithm can be found in: S. Lievens, B. De Baets, K. Cao-Van (2006). A Probabilistic Framework for the Design of Instance-Based Supervised Ranking Algorithms in an Ordinal Setting. Annals of Operations Research; Kim Cao-Van (2003). Supervised ranking: from semantics to algorithms; Stijn Lievens (2004). Studie en implementatie van instantie-gebaseerde algoritmen voor gesuperviseerd rangschikken
更新时间: 1970-01-01 08:00nz.ac.waikato.cms.weka » paceRegression
Class for building pace regression linear models and using them for prediction. Under regularity conditions, pace regression is provably optimal when the number of coefficients tends to infinity. It consists of a group of estimators that are either overall optimal or optimal under certain conditions. The current work of the pace regression theory, and therefore also this implementation, do not handle: - missing values - non-binary nominal attributes - the case that n - k is small where n is the number of instances and k is the number of coefficients (the threshold used in this implmentation is 20) For more information see: Wang, Y (2000). A new approach to fitting linear models in high dimensional spaces. Hamilton, New Zealand. Wang, Y., Witten, I. H.: Modeling for opti
更新时间: 1970-01-01 08:00nz.ac.waikato.cms.weka » prefuseTree
A visualization component for displaying tree structures from those schemes that can output trees (e.g. decision tree learners, Cobweb clusterer etc.). This component is available from the popup menu in the Explorer's classify and cluster panels. The component uses the prefuse visualization library.
更新时间: 1970-01-01 08:00| 仓库 | 个数 |
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