I am trying to find which classification methods, that do not use a training phase, are available. The scenario is gene expression based classification, in which you have a matrix of gene expression of m genes (features) and n samples (observations).A signature for each class is also provided (that is a list of the features to consider to define to which class belongs a sample).An application (non-training) is the Nearest Template Prediction method. In this case it is computed the cosine distance between each sample and each signature (on the common set of features). Then each sample is assigned to the nearest class (the sample-class comparison resulting in a smaller distance). No already classified samples are needed in this case.A different application (training) is the kNN method, in which we have a set of already labeled samples. Then, each new sample is labeled depending on how are labeled the k nearest samples.Are there any other non-training methods?Thanks
Which non-training classification methods are available?
classification
What you are asking about is Instance-Based Learning. k-Nearest Neighbors (kNN) appears to be the most popular of these methods and is applicable to a wide variety of problem domains. Another general type of instance-based learning is Analogical Modeling, which uses instances as exemplars for comparison with new data.You referred to kNN as an application that uses training but that is not correct (the Wikipedia entry you linked is somewhat misleading in that regard). Yes, there are training examples (labeled instances) but the classifier doesn't learn/train from these data. Rather, they are only used whenever you actually want to classify a new instance, which is why it is considered a lazy learner.Note that the Nearest Template Prediction method you mention effectively is a form of kNN with k=1 and cosine distance as the distance measure.