In a cognitive vision system, learning is expected to be a continuous process, which treats input images and pixels selectively. In this paper we present a method for subspace learning, which takes these considerations into account. First, we present a generalized PCA approach, which estimates the principal subspace considering weighted pixels and images. Next, we propose a method for increment...

Important reenements of concept learning in the limit from positive data considerably restricting the accessibility of input data are studied. Let c be any concept; every innnite sequence of elements exhausting c is called positive presentation of c. In all learning models considered the learning machine computes a sequence of hypotheses about the target concept from a positive presentation of ...

Population-based Incremental Learning is shown require very sensitive scaling of its learning rate. The learning rate must scale with the system size in a problem-dependent way. This is shown in two problems: the needle-in-a haystack, in which the learning rate must vanish exponentially in the system size, and in a smooth function in which the learning rate must vanish like the square root of t...

In this paper we propose a meta-evolutionary approach to improve on the performance of individual classifiers. In the proposed system, individual classifiers evolve, competing to correctly classify test points, and are given extra rewards for getting difficult points right. Ensembles consisting of multiple classifiers also compete for member classifiers, and are rewarded based on their predicti...

The biometrics authentication systems take attentions to cover the weakness of password authentication system. In this paper, we focus attention on the multi modal-biometrics of behavior characteristics. For the integration of multi modal biometrics Supervised Pareto learning SOM(SP-SOM) and its incremental learning method for implementing adaptive authentication system are proposed. Key–Words:...

Incremental Support Vector Machines (SVM) are instrumental in practical applications of online learning. This work focuses on the design and analysis of efficient incremental SVM learning, with the aim of providing a fast, numerically stable and robust implementation. A detailed analysis of convergence and of algorithmic complexity of incremental SVM learning is carried out. Based on this analy...

This paper has presented an approach to dealing with the complexity of explanation-based learning plans in complex domains. This approach uses a simplified algorithm to construct plans, and employs later refinements to repair bugs in constructed plans. This algorithm has the theoretical properties of completeness and convergence upon soundness. This incremental reasoning planning and learning a...