In this study, I evaluate the performance of diagonal Confidence-Weighted (CW) online linear classification on the KDD Cup 1999 dataset for network intrusion detection systems (NIDS). This is a compatible relationship due to the large number of instances in NIDS datasets, as well as the constantly changing feature distributions. CW learning achieves approximately 92% accuracy on the KDD dataset...

After preliminary training to open a sliding door using their head and their paw, dogs were given a discrimination task in which they were rewarded with food for opening the door using the same method (head or paw) as demonstrated by their owner (compatible group), or for opening the door using the alternative method (incompatible group). The incompatible group, which had to counterimitate to r...

We used PET to test whether human premotor and posterior parietal areas can subserve basic sensorimotor integration and sensorimotor learning equivalently in response to auditory and visual stimuli, as has been shown in frontoparietal neurons in non-human primates. Normal subjects were studied while they performed a spatial compatibility task. They were instructed to respond to lateralized audi...

recently, zhang and song [q. zhang, y. song, fixed point theory forgeneralized $varphi$-weak contractions,appl. math. lett. 22(2009) 75-78] proved a common fixed point theorem for two mapssatisfying generalized $varphi$-weak contractions. in this paper, we prove a common fixed point theorem fora family of compatible maps. in fact, a new generalization of zhangand song's theorem is given.

Recent years have witnessed wide application of hashing for large-scale image retrieval. However, most existing hashing methods are based on hand-crafted features which might not be optimally compatible with the hashing procedure. Recently, deep hashing methods have been proposed to perform simultaneous feature learning and hashcode learning with deep neural networks, which have shown better pe...

In many structured prediction problems, the highest-scoring labeling is hard to compute exactly, leading to the use of approximate inference methods. However, when inference is used in a learning algorithm, a good approximation of the score may not be sufficient. We show in particular that learning can fail even with an approximate inference method with rigorous approximation guarantees. There ...

An unsupervised competitive learning algorithm based on the classical -means clustering algorithm is proposed. The proposed learning algorithm called the centroid neural network (CNN) estimates centroids of the related cluster groups in training date. This paper also explains algorithmic relationships among the CNN and some of the conventional unsupervised competitive learning algorithms includ...