346,185 research outputs found
A Family of Maximum Margin Criterion for Adaptive Learning
In recent years, pattern analysis plays an important role in data mining and
recognition, and many variants have been proposed to handle complicated
scenarios. In the literature, it has been quite familiar with high
dimensionality of data samples, but either such characteristics or large data
have become usual sense in real-world applications. In this work, an improved
maximum margin criterion (MMC) method is introduced firstly. With the new
definition of MMC, several variants of MMC, including random MMC, layered MMC,
2D^2 MMC, are designed to make adaptive learning applicable. Particularly, the
MMC network is developed to learn deep features of images in light of simple
deep networks. Experimental results on a diversity of data sets demonstrate the
discriminant ability of proposed MMC methods are compenent to be adopted in
complicated application scenarios.Comment: 14 page
Adaptive Resonance Theory: Self-Organizing Networks for Stable Learning, Recognition, and Prediction
Adaptive Resonance Theory (ART) is a neural theory of human and primate information processing and of adaptive pattern recognition and prediction for technology. Biological applications to attentive learning of visual recognition categories by inferotemporal cortex and hippocampal system, medial temporal amnesia, corticogeniculate synchronization, auditory streaming, speech recognition, and eye movement control are noted. ARTMAP systems for technology integrate neural networks, fuzzy logic, and expert production systems to carry out both unsupervised and supervised learning. Fast and slow learning are both stable response to large non stationary databases. Match tracking search conjointly maximizes learned compression while minimizing predictive error. Spatial and temporal evidence accumulation improve accuracy in 3-D object recognition. Other applications are noted.Office of Naval Research (N00014-95-I-0657, N00014-95-1-0409, N00014-92-J-1309, N00014-92-J4015); National Science Foundation (IRI-94-1659
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