SCUT_ROCCHIO - implements the Scut thresholding technique 
  from [1] for the Rocchio classifier
    THRESHOLD=SCUT_ROCCHIO(A, CLUSTERS, BETA, GAMMA, Q, 
    LABELS_TR, LABELS_TE, MINF1, NORMALIZE, STEPS) returns 
    the vector of thresholds for the Rocchio classifier 
    for the collection [A Q]. A and Q define the training 
    and test parts of the validation set with labels 
    LABELS_TR and LABELS_TE respectively. MINF1 defines 
    the minimum F1 value, while NORMALIZE defines if cosine 
    (1) or euclidean distance (0) measure of similarity is 
    to be used, CLUSTERS is a structure defining the classes 
    and STEPS defines the number of steps used during 
    thresholding. BETA and GAMMA define the weight of positive 
    and negative examples in the formation of each class 
    centroid.
    [THRESHOLD, F, THRESHOLDS]=SCUT_ROCCHIO(A, CLUSTERS, BETA, 
    GAMMA, Q, LABELS_TR, LABELS_TE, MINF1, NORMALIZE, STEPS) 
    returns also the best F1 value as well as the matrix of 
    thresholds for each step (row i corresponds to step i).
 
    REFERENCES: 
    [1] Y. Yang. A Study of Thresholding Strategies for Text 
    Categorization. In Proc. 24th ACM SIGIR, pages 137–145, 
    New York, NY, USA, 2001. ACM Press.
 
  Copyright 2011 Dimitrios Zeimpekis, Eugenia Maria Kontopoulou, 
                 Efstratios Gallopoulos
					
				

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