Abstract. We focus on the problem of clustering with soft instancelevel constraints. Recently, the CVQE algorithm was proposed in this context. It modifies the objective function of traditional K-means to include penalties for violated constraints. CVQE was shown to efficiently produce high-quality clustering of UCI data. In this work, we examine the properties of CVQE and propose a modification that results in a more intuitive objective function, with lower computational complexity. We present our extensive experimentation, which provides insight into CVQE and shows that our new variant can dramatically improve clustering quality while reducing run time. We show its superiority in a large-scale surveillance scenario with noisy constraints.
Learning agents, whether natural or artificial, must update their internal parameters in order to improve their behavior over time. In reinforcement learning, this plasticity is influenced by an environmental signal, termed a reward, that directs the changes in appropriate directions. We apply a recently introduced policy learning algorithm from machine learning to networks of spiking neurons and derive a spike-time-dependent plasticity rule that ensures convergence to a local optimum of the expected average reward. The approach is applicable to a broad class of neuronal models, including the Hodgkin-Huxley model. We demonstrate the effectiveness of the derived rule in several toy problems. Finally, through statistical analysis, we show that the synaptic plasticity rule established is closely related to the widely used BCM rule, for which good biological evidence exists.
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