There exist many different generalization error bounds for classification. Each of these bounds contains an improvement over the others for certain situations.Our goal is to combine these different improvements into a single bound. In particular we combine the PAC-Bayes approach introduced byMcAllester [1], which is interesting for averaging classifiers, with the optimal union bound provided by the generic chaining technique developed by Fernique and Talagrand [2]. This combination is quite natural sincethe generic chaining is based on the notion of majorizing measures, whichcan be considered as priors on the set of classifiers, and such priors also arise in the PACbayesian setting.

The weight change scheme of the STDP synapses can be set to either weight-independent or weight-dependent mode. We present results that characterise the learning window implemented for both modes of operation. When presented with spike trains with different types of synchronisation the neurons develop bimodal weight distributions. We also show that a 2-layered network of silicon spiking neurons with STDP synapses can perform hierarchical synchrony detection.

Knowledge about possible state transitions is encoded in the lateral connectivity.

We attempt to understand visual classification in humans using both psychophysical andmachine learning techniques. Frontal views of human faces were used for a gender classification task. Human subjects classified thefaces and their gender judgment, reaction time and confidence rating were recorded. Several hyperplane learning algorithms were used on the same classification task using the Principal Components of the texture and shape representation of the faces. The classification performance ofthe learning algorithms was estimated using the face database with the true gender of the faces as labels, and also with the gender estimated bythe subjects.

Brain-Computer Interfaces (BCI) are an interesting emerging technology that is driven by the motivation to develop an effective communication interface translatinghuman intentions into a control signal for devices like computers or neuroprostheses. If this can be done bypassing the usual human outputpathways like peripheral nerves and muscles it can ultimately become a valuable tool for paralyzed patients.

We examine the use of hidden Markov and hidden semi-Markov models forautomatically segmenting an electrocardiogram waveform into its constituent waveform features. An undecimated wavelet transform is used to generate an overcomplete representation of the signal that is more appropriate for subsequent modelling. We show that the state durations implicitin a standard hidden Markov model are ill-suited to those of real ECG features, and we investigate the use of hidden semi-Markov models for improved state duration modelling.

The bootstrap has become a popular method for exploring model (structure) uncertainty. Our experiments with artificial and realworld datademonstrate that the graphs learned from bootstrap samples can be severely biased towards too complex graphical models. Accountingfor this bias is hence essential, e.g., when exploring model uncertainty. We find that this bias is intimately tied to (well-known) spurious dependences induced by the bootstrap. The leading-order bias-correction equals one half of Akaike's penalty for model complexity. We demonstrate the effect of this simple bias-correction in our experiments. We also relate this bias to the bias of the plugin estimator for entropy, as well as to the difference betweenthe expected test and training errors of a graphical model, which asymptotically equals Akaike's penalty (rather than one half).

We present a modified version of the perceptron learning algorithm (PLA) which solves semidefinite programs (SDPs) in polynomial time. The algorithm is based on the following three observations: (i) Semidefinite programs are linear programs with infinitely many (linear) constraints; (ii) every linear program can be solved by a sequence of constraint satisfaction problems with linear constraints; (iii) in general, the perceptron learning algorithm solves a constraint satisfaction problem with linear constraints in finitely many updates. Combining the PLA with a probabilistic rescaling algorithm (which, on average, increases the size of the feasable region) results in a probabilistic algorithmfor solving SDPs that runs in polynomial time. We present preliminary results which demonstrate that the algorithm works,but is not competitive with state-of-the-art interior point methods.

We consider the problem of reconstructing patterns from a feature map. Learning algorithms using kernels to operate in a reproducing kernel Hilbert space (RKHS) express their solutions in terms of input points mapped into the RKHS. We introduce a technique based on kernel principal componentanalysis and regression to reconstruct corresponding patterns inthe input space (aka pre-images) and review its performance in several applications requiring the construction of pre-images. The introduced techniqueavoids difficult and/or unstable numerical optimization, is easy to implement and, unlike previous methods, permits the computation ofpre-images in discrete input spaces.

The E-step boils down to computing probabilities of the hidden variables given the observed variables (evidence) and current set of parameters. The M-step then, given these probabilities, yields a new set of parameters guaranteed to increase the likelihood. In Bayesian networks, that will be the focus of this article, the M-step is usually relatively straightforward. A complication may arise in the E-step, when computing the probability of the hidden variables given the evidence becomes intractable. An often used approach is to replace the exact yet intractable inference in the E step with approximate inference, either through sampling or using a deterministic variational method. The use of a "mean-field" variational method in this context leads to an algorithm known as variational EM and can be given theinterpretation of minimizing a free energy with respect to both a tractable approximate distribution (approximate E-step) and the parameters (M-step) [2]. Loopy belief propagation [3] and variants thereof, such as generalized belief propagation [4]and expectation propagation [5], have become popular alternatives to the "mean-field" variational approaches, often yielding somewhat better approximations. Andindeed, they can and have been applied for approximate inference in the E-step of the EM algorithm (see e.g.