We review Boltzmann machines extended for time-series. These models often have recurrent structure, and back propagration through time (BPTT) is used to learn their parameters. The perstep computational complexity of BPTT in online learning, however, grows linearly with respect to the length of preceding time-series (i.e., learning rule is not local in time), which limits the applicability of B...

Previous research reveals that older adults sometimes show enhanced processing of emotionally positive stimuli relative to negative stimuli, but that this positivity bias reverses to become a negativity bias when cognitive control resources are less available. In this study, we test the hypothesis that emotionally positive feedback will attenuate well-established age-related deficits in rule le...

A new learning rule (Precise-Spike-Driven (PSD) Synaptic Plasticity) is proposed for processing and memorizing spatiotemporal patterns. PSD is a supervised learning rule that is analytically derived from the traditional Widrow-Hoff rule and can be used to train neurons to associate an input spatiotemporal spike pattern with a desired spike train. Synaptic adaptation is driven by the error betwe...

Contrast set learning is important to discover control variables that can distinguish different groups in a dataset. Association rule mining has an inherent connection to the contrast set learning problem and has also been used to address it. All of the association rule based contrast set learning techniques use support-confidence based methods and inherit their limitations. In recent years sta...

Rules encompass cue-action-outcome associations used to guide decisions and strategies in a specific context. Subregions of the frontal cortex including the orbitofrontal cortex (OFC) and dorsomedial prefrontal cortex (dmPFC) are implicated in rule learning, although changes in structural connectivity underlying rule learning are poorly understood. We imaged OFC axonal projections to dmPFC duri...

This work promotes a novel point of view in rough set applications: rough sets rule learning for ordinal prediction is based on rough graphical representation of the rules. Our approach tackles two barriers of rule learning. Unlike in typical rule learning, we construct ordinal prediction with a mathematical approach, rough sets, rather than purely rule quality measures. This construction resul...

fuzzy rule-based classification system (frbcs) is a popular machine learning technique for classification purposes. one of the major issues when applying it on imbalanced data sets is its biased to the majority class, such that, it performs poorly in respect to the minority class. however many cases the minority classes are more important than the majority ones. in this paper, we have extended ...

A extension to the k-nearest neighbor classifier is described in which automatically induced rules are used as binary features, which are active in an instance when the left-hand side of the corresponding rule matches with the instance. The ripper rule induction algorithm is employed to produce the rules. The similarity between a memory instance and a new instance is based on the rules the two ...

A general learning rule, “BCM-δ”, is proposed that subsumes both unsupervised learning as a form of the BCM rule (Bienenstock, Cooper, Munro, 1982; Munro, 1984) and the delta rule (Rosenblatt, 1958; Rumelhart, Hinton, and Williams, 1986). The “BCM-δ” unit is composed of two subunits, T and L, each integrating distinct input streams across distinct sets of synapses. The two subunits follow a com...