Titlebook: Algorithmic Learning Theory; 12th International C Naoki Abe,Roni Khardon,Thomas Zeugmann Conference proceedings 2001 Springer-Verlag Berlin

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A Random Sampling Technique for Training Support Vector Machinesues for training support vector machines (more precisely, primal-form maximal-margin classifiers) that solve two-group classification problems by using hyperplane classifiers. Through this research, we are aiming (I) to design efficient and theoretically guaranteed support vector machine training al

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Learning Coherent Conceptsitive learning situations, where “natural” constraints are imposed on the outcomes of classifiers so that a valid sentence, image or any other domain representation is produced. We formalize these learning situations, after a model suggested in [.] and study generalization abilities of learning algo

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Learning Intermediate Conceptsn some situations, although the target concept may be quite complex when expressed as a function of the attribute values of the instance, it may have a simple relationship with some intermediate (yet to be learned) concepts. In such cases, it may be advantageous to learn both these intermediate conc

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