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AODE
Averaged one-dependence estimators (AODE) is a probabilistic classification learning technique. It was developed to address the attribute-independence problem of the popular naive Bayes classifier. It frequently develops substantially more accurate classifiers than naive Bayes at the cost of a modest increase in the amount of computation.Webb, G. I., J. Boughton, and Z. Wang (2005)"Not So Naive Bayes: Aggregating One-Dependence Estimators" ''Machine Learning'', 58(1), 5–24. The AODE classifier AODE seeks to estimate the probability of each class ''y'' given a specified set of features ''x''1, ... ''x''n, P(''y'' , ''x''1, ... ''x''n). To do so it uses the formula :\hat(y\mid x_1, \ldots x_n)=\frac where \hat(\cdot) denotes an estimate of P(\cdot), F(\cdot) is the frequency with which the argument appears in the sample data and ''m'' is a user specified minimum frequency with which a term must appear in order to be used in the outer summation. In recent practice ''m'' is u ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Naive Bayes Classifier
In statistics, naive Bayes classifiers are a family of simple "probabilistic classifiers" based on applying Bayes' theorem with strong (naive) independence assumptions between the features (see Bayes classifier). They are among the simplest Bayesian network models, but coupled with kernel density estimation, they can achieve high accuracy levels. Naive Bayes classifiers are highly scalable, requiring a number of parameters linear in the number of variables (features/predictors) in a learning problem. Maximum-likelihood training can be done by evaluating a closed-form expression, which takes linear time, rather than by expensive iterative approximation as used for many other types of classifiers. In the statistics literature, naive Bayes models are known under a variety of names, including simple Bayes and independence Bayes. All these names reference the use of Bayes' theorem in the classifier's decision rule, but naive Bayes is not (necessarily) a Bayesian method. Introductio ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Classifier (mathematics)
In statistics, classification is the problem of identifying which of a set of categories (sub-populations) an observation (or observations) belongs to. Examples are assigning a given email to the "spam" or "non-spam" class, and assigning a diagnosis to a given patient based on observed characteristics of the patient (sex, blood pressure, presence or absence of certain symptoms, etc.). Often, the individual observations are analyzed into a set of quantifiable properties, known variously as explanatory variables or ''features''. These properties may variously be categorical (e.g. "A", "B", "AB" or "O", for blood type), ordinal (e.g. "large", "medium" or "small"), integer-valued (e.g. the number of occurrences of a particular word in an email) or real-valued (e.g. a measurement of blood pressure). Other classifiers work by comparing observations to previous observations by means of a similarity or distance function. An algorithm that implements classification, especially in a ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Incremental Learning
In computer science, incremental learning is a method of machine learning in which input data is continuously used to extend the existing model's knowledge i.e. to further train the model. It represents a dynamic technique of supervised learning and unsupervised learning that can be applied when training data becomes available gradually over time or its size is out of system memory limits. Algorithms that can facilitate incremental learning are known as incremental machine learning algorithms. Many traditional machine learning algorithms inherently support incremental learning. Other algorithms can be adapted to facilitate incremental learning. Examples of incremental algorithms include decision trees (IDE4, ID5R angaenari, decision rules, artificial neural networks ( RBF networks, Learn++, Fuzzy ARTMAP, TopoART,Marko Tscherepanow, Marco Kortkamp, and Marc KammerA Hierarchical ART Network for the Stable Incremental Learning of Topological Structures and Associations from Noisy Dat ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Weka (machine Learning)
Waikato Environment for Knowledge Analysis (Weka), developed at the University of Waikato, New Zealand, is free software licensed under the GNU General Public License, and the companion software to the book "Data Mining: Practical Machine Learning Tools and Techniques". Description Weka contains a collection of visualization tools and algorithms for data analysis and predictive modeling, together with graphical user interfaces for easy access to these functions. The original non-Java version of Weka was a Tcl/ Tk front-end to (mostly third-party) modeling algorithms implemented in other programming languages, plus data preprocessing utilities in C, and a makefile-based system for running machine learning experiments. This original version was primarily designed as a tool for analyzing data from agricultural domains, but the more recent fully Java-based version (Weka 3), for which development started in 1997, is now used in many different application areas, in particular for educat ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Cluster-weighted Modeling
In data mining, cluster-weighted modeling (CWM) is an algorithm-based approach to non-linear prediction of outputs (dependent variables) from inputs (independent variables) based on density estimation using a set of models (clusters) that are each notionally appropriate in a sub-region of the input space. The overall approach works in jointly input-output space and an initial version was proposed by Neil Gershenfeld. Basic form of model The procedure for cluster-weighted modeling of an input-output problem can be outlined as follows. In order to construct predicted values for an output variable ''y'' from an input variable ''x'', the modeling and calibration procedure arrives at a joint probability density function, ''p''(''y'',''x''). Here the "variables" might be uni-variate, multivariate or time-series. For convenience, any model parameters are not indicated in the notation here and several different treatments of these are possible, including setting them to fixed values as a s ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Classification Algorithms
Classification is a process related to categorization, the process in which ideas and objects are recognized, differentiated and understood. Classification is the grouping of related facts into classes. It may also refer to: Business, organizations, and economics * Classification of customers, for marketing (as in Master data management) or for profitability (e.g. by Activity-based costing) * Classified information, as in legal or government documentation * Job classification, as in job analysis * Standard Industrial Classification, economic activities Mathematics * Attribute-value system, a basic knowledge representation framework * Classification theorems in mathematics * Mathematical classification, grouping mathematical objects based on a property that all those objects share * Statistical classification, identifying to which of a set of categories a new observation belongs, on the basis of a training set of data Media * Classification (literature), a figure of speech li ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Bayesian Estimation
In estimation theory and decision theory, a Bayes estimator or a Bayes action is an estimator or decision rule that minimizes the posterior expected value of a loss function (i.e., the posterior expected loss). Equivalently, it maximizes the posterior expectation of a utility function. An alternative way of formulating an estimator within Bayesian statistics is maximum a posteriori estimation. Definition Suppose an unknown parameter \theta is known to have a prior distribution \pi. Let \widehat = \widehat(x) be an estimator of \theta (based on some measurements ''x''), and let L(\theta,\widehat) be a loss function, such as squared error. The Bayes risk of \widehat is defined as E_\pi(L(\theta, \widehat)), where the expectation is taken over the probability distribution of \theta: this defines the risk function as a function of \widehat. An estimator \widehat is said to be a ''Bayes estimator'' if it minimizes the Bayes risk among all estimators. Equivalently, the estimator whic ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
