In this task, you are given a part of an article. Your task is to generate headline (title) for this text. Preferred headlines are under fifteen words.

[Q]: There is a brief description of the probabilistic causal graph model for representing, reasoning with, and learn ing causal structure using Bayesian networks. It is then argued that this model is closely related to how humans reason with and learn causal structure. It is shown that studies in psychology on discounting (reasoning concern ing how the presence of one cause of an effect makes an other cause less probable) support the hypothesis that humans reach the same judgments as algorithms for do ing inference in Bayesian networks. Next, it is shown how studies by Piaget indicate that humans learn causal structure by observing the same independencies and de pendencies as those used by certain algorithms for learn ing the structure of a Bayesian network. Based on this indication, a subjective definition of causality is for warded. Finally, methods for further testing the accu racy of these claims are discussed.
[A]: THE CoGNITIVE PROCESSING OF CAUSAL KNOWLEDGE

[Q]: We propose a localized approach to multiple kernel learning that can be formulated as a convex optimization problem over a given cluster structure. For which we obtain generalization error guarantees and derive an optimization algorithm based on the Fenchel dual representation. Experiments on real-world datasets from the application domains of computational biology and computer vision show that convex localized multiple kernel learning can achieve higher prediction accuracies than its global and non-convex local counterparts.
[A]: Localized Multiple Kernel LearningA Convex Approach

[Q]: This work proposes a low complexity nonlinearity model and develops adaptive algorithms over it. The model is based on the decomposableor rank-one, in tensor language Volterra kernels. It may also be described as a product of FIR filters, which explains its low-complexity. The rank-one model is also interesting because it comes from a well-posed problem in approximation theory. The paper uses such model in an estimation theory context to develop an exact gradienttype algorithm, from which adaptive algorithms such as the least mean squares (LMS) filter and its data-reuse versionthe TRUE-LMSare derived. Stability and convergence issues are addressed. The algorithms are then tested in simulations, which show its good performance when compared to other nonlinear processing algorithms in the literature.
[A]:
Nonlinear Adaptive Algorithms on Rank-One Tensor Models