Multi-class SVMs: From Tighter Data-Dependent Generalization Bounds to Novel Algorithms

This paper studies the generalization performance of multi-class classification algorithms, for which we obtain, for the first time, a data-dependent generalization error bound with a logarithmic dependence on the class size, substantially improving the state-of-the-art linear dependence in the existing data-dependent generalization analysis. The theoretical analysis motivates us to introduce a new multi-class classification machine based on $\ell_p$-norm regularization, where the parameter $p$ controls the complexity of the corresponding bounds. We derive an efficient optimization algorithm based on Fenchel duality theory. Benchmarks on several real-world datasets show that the proposed algorithm can achieve significant accuracy gains over the state of the art

Review: The Efficacy of Cannabidiol (CBD) as Potential Antipsychotic Medication

Psychotic disorders such as schizophrenia are widespread and severely disabling; however, current pharmacological treatments are unsatisfactory due to major side effects. The current review discusses the therapeutic potential of cannabidiol (CBD), a non-psychoactive component of cannabis, as an antipsychotic drug. Research lines including studies based on animal models of psychosis, human experimental studies, neuroimaging studies, epidemiological studies, and clinical studies are reviewed. The studies described provide empirical support for the antipsychotic effects of CBD and indicate reduced side effects, high tolerability, and superior cost-effectiveness compared to regular antipsychotic medication. It is concluded that CBD may prove a safe and attractive alternative treatment for psychotic conditions. However, current evidence largely stems from experimental, non-clinical studies. Large-scale randomized clinical trials are needed before this can be implemented in practice