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Example of a naive Bayes classifier depicted as a Bayesian Network. In statistics, naive Bayes classifiers are a family of linear "probabilistic classifiers" which assumes that the features are conditionally independent, given the target class. The strength (naivety) of this assumption is what gives the classifier its name.
Standard examples of each, all of which are linear classifiers, are: generative classifiers: naive Bayes classifier and; linear discriminant analysis; discriminative model: logistic regression; In application to classification, one wishes to go from an observation x to a label y (or probability distribution on labels).
Automatically learning the graph structure of a Bayesian network (BN) is a challenge pursued within machine learning. The basic idea goes back to a recovery algorithm developed by Rebane and Pearl [ 7 ] and rests on the distinction between the three possible patterns allowed in a 3-node DAG:
A training data set is a data set of examples used during the learning process and is used to fit the parameters (e.g., weights) of, for example, a classifier. [9] [10]For classification tasks, a supervised learning algorithm looks at the training data set to determine, or learn, the optimal combinations of variables that will generate a good predictive model. [11]
It can be drastically simplified by assuming that the probability of appearance of a word knowing the nature of the text (spam or not) is independent of the appearance of the other words. This is the naive Bayes assumption and this makes this spam filter a naive Bayes model. For instance, the programmer can assume that:
The main advantage gained in employing a lazy learning method is that the target function will be approximated locally, such as in the k-nearest neighbor algorithm. Because the target function is approximated locally for each query to the system, lazy learning systems can simultaneously solve multiple problems and deal successfully with changes ...
Formally, an "ordinary" classifier is some rule, or function, that assigns to a sample x a class label ลท: ^ = The samples come from some set X (e.g., the set of all documents, or the set of all images), while the class labels form a finite set Y defined prior to training.
Bayesian learning mechanisms are probabilistic causal models [1] used in computer science to research the fundamental underpinnings of machine learning, and in cognitive neuroscience, to model conceptual development. [2] [3]