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Extra resources for Student Guide 801 2.0
Example text
Insert the first object into a new cluster, use the object as the mode of the cluster, and remove the object from S. 3. Initialize φ to 1. 4. Loop through the following until S is empty or φ > threshold a. For each object o in S i. Find o’s nearest cluster c by using the dissimilarity metric to compare o with the modes of all existing cluster(s). ii. If the number of different values between o and c’s mode is larger than φ, insert o into a new cluster iii. Otherwise, insert o into c and update c’s mode.
Fi denotes the corresponding feature space of representation i and ”−” denotes that there is no object 26 J. Aßfalg et al. description for this representation. Missing representations are a quite common problem in many application areas and thus, should be considered when building a method. Obviously, for each object there has to be at least one oj = ”−”. For a given set of classes C = ci , . . , ck , our classification task can be described in the following way. Given a training set of multi-represented objects T R ⊂ R1 × .
De Abstract. Complex objects are often described by multiple representations modeling various aspects and using various feature transformations. To integrate all information into classification, the common way is to train a classifier on each representation and combine the results based on the local class probabilities. In this paper, we derive so-called confidence estimates for each of the classifiers reflecting the correctness of the local class prediction and use the prediction having the maximum confidence value.
Student Guide 801 2.0
by James
4.4