Many works have shown that strong connections relate learning from examples toregularization techniques for ill-posed inverse problems. Nevertheless bynow there was no formal evidence neither that learning from examples could be seen as an inverse problem nor that theoretical results in learning theory could be independently derived using tools from regularization theory.In this paper we provide a positive answer to both questions. Indeed, considering the square loss, we translate the learning problem in the language of regularization theory and show that consistency resultsand optimal regularization parameter choice can be derived by the discretization of the corresponding inverse problem.

We show that anomaly detection can be interpreted as a binary classification problem.Using this interpretation we propose a support vector machine (SVM) for anomaly detection. We then present some theoretical resultswhich include consistency and learning rates. Finally, we experimentally compare our SVM with the standard one-class SVM.

We present a discriminative part-based approach for the recognition of object classes from unsegmented cluttered scenes. Objects are modeled as flexible constellations of parts conditioned on local observations found by an interest operator. For each object class the probability of a given assignment of parts to local features is modeled by a Conditional Random Field(CRF). We propose an extension of the CRF framework that incorporates hidden variables and combines class conditional CRFs into a unified framework for part-based object recognition. The parameters of the CRF are estimated in a maximum likelihood framework and recognition proceedsby finding the most likely class under our model. The main advantage of the proposed CRF framework is that it allows us to relax the assumption of conditional independence of the observed data (i.e.

Learning in a multiagent system is a challenging problem due to two key factors. First, if other agents are simultaneously learning then the environment isno longer stationary, thus undermining convergence guarantees. Second, learning is often susceptible to deception, where the other agents may be able to exploit a learner's particular dynamics. In the worst case, this could result in poorer performance than if the agent was not learning at all. These challenges are identifiable in the two most common evaluationcriteria for multiagent learning algorithms: convergence and regret. Algorithms focusing on convergence or regret in isolation are numerous. In this paper, we seek to address both criteria in a single algorithm by introducing GIGA-WoLF, a learning algorithm for normalform games.We prove the algorithm guarantees at most zero average regret, while demonstrating the algorithm converges in many situations of self-play. We prove convergence in a limited setting and give empirical resultsin a wider variety of situations. These results also suggest a third new learning criterion combining convergence and regret, which we call negative non-convergence regret (NNR).

Saliency mechanisms play an important role when visual recognition must be performed in cluttered scenes. We propose a computational definition ofsaliency that deviates from existing models by equating saliency to discrimination. In particular, the salient attributes of a given visual class are defined as the features that enable best discrimination between that class and all other classes of recognition interest. It is shown that this definition leads to saliency algorithms of low complexity, that are scalable to large recognition problems, and is compatible with existing models of early biological vision. Experimental results demonstrating success in the context of challenging recognition problems are also presented.

This paper presents a neuromorphic model of two olfactory signalprocessing primitives:chemotopic convergence of olfactory receptor neurons, and center on-off surround lateral inhibition in the olfactory bulb. A self-organizing model of receptor convergence onto glomeruli is used to generate a spatially organized map, an olfactory image. This map serves as input to a lattice of spiking neurons with lateral connections. The dynamics of this recurrent network transforms the initial olfactory image into a spatiotemporal pattern that evolves and stabilizes into odor-and intensity-coding attractors.

The game of G0 originated in China over 4000 years ago.

In this paper, we propose a new method, Parametric Embedding (PE), for visualizing the posteriors estimated over a mixture model.

We devise and experiment with a dynamical kernel-based system for tracking hand movements from neural activity. The state of the system corresponds to the hand location, velocity, and acceleration, while the system's input are the instantaneous spike rates. The system's state dynamics isdefined as a combination of a linear mapping from the previous estimated state and a kernel-based mapping tailored for modeling neural activities. In contrast to generative models, the activity-to-state mapping is learned using discriminative methods by minimizing a noise-robust loss function. We use this approach to predict hand trajectories on the basis of neural activity in motor cortex of behaving monkeys and find that the proposed approach is more accurate than both a static approach based on support vector regression and the Kalman filter.

We have a set of 71 training pairs 27,-, yi, Where x,- 6 R" is a p-Vector of real valued predictors