In this paper we investigate the relation between transfer learning in reinforcement learning with function approximation and supervised learning with concept drift. We present a n...
We propose a network model of spiking neurons, without preimposed topology and driven by STDP (Spike-Time-Dependent Plasticity), a temporal Hebbian unsupervised learning mode, biol...
We describe a case study in tit(', application of symbolic machinc learning techniques for the discow;ry of linguistic rules and categories. A supervised rule induction algor...
We derive two variants of a semi-supervised model for fine-grained sentiment analysis. Both models leverage abundant natural supervision in the form of review ratings, as well as...
This paper presents a supervised machine learning approach for summarizing legal documents. A commercial system for the analysis and summarization of legal documents provided us wi...