1,721,057 research outputs found
Computational capabilities of local-feedback recurrent networks acting as finite-state machines
No full textIn this paper we explore the expressive power of recurrent networks with local feedback connections for symbolic data streams, We rely on the analysis of the maximal set of strings that can be shattered by the concept class associated to these networks (i.e., strings that can be arbitrarily classified as positive or negative), and find that their expressive power is inherently limited, since there are sets of strings that cannot be shattered, regardless of the number of hidden units. Although the analysis holds for networks with hard threshold units, we claim that the incremental computational capabilities gained when using sigmoidal units are severely paid in terms of robustness of the corresponding representation
Multilayered networks and the C-G uncertainty principleProceedings of SPIE
No full textThe experience gained in many experiments with neural networks has shown that many challenging problems are still hard to solve, since the learning process becomes very slow, often leading to suboptimal solutions. In this paper we analyze this problem for the case of two-layered networks by discussing on the joint behavior of the algorithm convergence and the generalization to new data. We suggest two scores for generalization and optimal convergence that behave like conjugate variable in Quantum Mechanics. As a result, the requirement of increasing the generalization is likely to affect the optimal convergence. This suggests that "difficult" problems are better faced with biased-models, somewhat tuned on the task to be solved. © 1993 SPIE. All rights reserved
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics): Preface
No full text
Inductive Logic Programming - 20th International Conference, ILP 2010, Florence, Italy, June 27-30, 2010. Revised Papers
No full textApproximation and generalization issues of recurrent networks dealing with structured data
No full textHammer B. Approximation and generalization issues of recurrent networks dealing with structured data. In: Frasconi P, Sperduti A, Gori M, eds. ECAI workshop: Foundations of connectionist-symbolic integration: representation, paradigms, and algorithms. 2000
Short-Term Traffic Flow Forecasting: An Experimental Comparison of Time-Series Analysis and Supervised Learning
No full textThe literature on short-term traffic flow forecasting has undergone great development recently. Many works, describing a wide variety of different approaches, which very often share similar features and ideas, have been published. However, publications presenting new prediction algorithms usually employ different settings, data sets, and performance measurements, making it difficult to infer a clear picture of the advantages and limitations of each model. The aim of this paper is twofold. First, we review existing approaches to short-term traffic flow forecasting methods under the common view of probabilistic graphical models, presenting an extensive experimental comparison, which proposes a common baseline for their performance analysis and provides the infrastructure to operate on a publicly available data set. Second, we present two new support vector regression models, which are specifically devised to benefit from typical traffic flow seasonality and are shown to represent an interesting compromise between prediction accuracy and computational efficiency. The SARIMA model coupled with a Kalman filter is the most accurate model; however, the proposed seasonal support vector regressor turns out to be highly competitive when performing forecasts during the most congested periods
Local feedback multilayered networks
No full textIn this paper, we investigate the capabilities of local feedback multilayered networks, a particular class of recurrent networks, in which feedback connections are only allowed from neurons to themselves. In this class, learning can be accomplished by an algorithm that is local in both space and time. We describe the limits and properties of these networks and give some insights on their use for solving practical problems
Hidden Tree Markov Models for Image Document Classification
No full textClassification is an important problem in image document processing and is often a preliminary step toward recognition, understanding, and information extraction. In this paper, the problem is formulated in the framework of concept learning and each category corresponds to the set of image documents with similar physical structure. We propose a solution based on two algorithmic ideas. First, we obtain a structured representation of images based on labeled XY-trees (this representation informs the learner about important relationships between image subconstituents). Second, we propose a probabilistic architecture that extends hidden Markov models for learning probability distributions defined on spaces of labeled trees. Finally, a successful application of this method to the categorization of commercial invoices is presented
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