Thompson sampling has emerged as an effective heuristic for a broad range of online decision problems. In its basic form, the algorithm requires computing and sampling from a posterior distribution over models, which is tractable only for simple special cases. This paper develops ensemble sampling, which aims to approximate Thompson sampling while maintaining tractability even in the face of co...

Tractography uses diffusion MRI to estimate the trajectory and cortical projection zones of white matter fascicles in the living human brain. There are many different tractography algorithms and each requires the user to set several parameters, such as curvature threshold. Choosing a single algorithm with specific parameters poses two challenges. First, different algorithms and parameter values...

The idea of ensemble learning is to employ multiple learners and combine their predictions. There is no definitive taxonomy. Jain, Duin and Mao (2000) list eighteen classifier combination schemes; Witten and Frank (2000) detail four methods of combining multiple models: bagging, boosting, stacking and errorcorrecting output codes whilst Alpaydin (2004) covers seven methods of combining multiple...

An ensemble contains a number of learners which are usually called base learners. The generalization ability of an ensemble is usually much stronger than that of base learners. Actually, ensemble learning is appealing because that it is able to boost weak learners which are slightly better than random guess to strong learners which can make very accurate predictions. So, “base learners” are als...

Ensemble methods that train multiple learners and then combine their predictions have been shown to be very effective in supervised learning. This paper explores ensemble methods for unsupervised learning. Here an ensemble comprises multiple clusterers, each of which is trained by k-means algorithm with different initial points. The clusters discovered by different clusterers are aligned, i.e. ...

A common problem in many areas of large-scale machine learning involves manipulation of a large matrix. This matrix may be a kernel matrix arising in Support Vector Machines [9, 15], Kernel Principal Component Analysis [47] or manifold learning [43,51]. Large matrices also naturally arise in other applications, e.g., clustering, collaborative filtering, matrix completion, and robust PCA. For th...

This chapter gives a tutorial introduction to Ensemble Learning, a recently developed Bayesian method. For many problems it is intractable to perform inferences using the true posterior density over the unknown variables. Ensemble Learning allows the true posterior to be approximated by a simpler approximate distribution for which the required inferences are tractable.

The possibility of teleportation is by sure the most interesting consequence of quantum non-separability. So far, however, teleportation schemes have been formulated by use of state vectors and considering individual entities only. In the present article the feasibility of teleportation is examined on the basis of the rigorous ensemble interpretation of quantum mechanics (not to be confused wit...

This note presents a chronological review of the literature on ensemble learning which has accumulated over the past twenty years. The idea of ensemble learning is to employ multiple learners and combine their predictions. If we have a committee of M models with uncorrelated errors, simply by averaging them the average error of a model can be reduced by a factor of M. Unfortunately, the key ass...