We propose a cascaded linear model for joint Chinese word segmentation and partof-speech tagging. With a character-based perceptron as the core, combined with realvalued features such as language models, the cascaded model is able to efficiently utilize knowledge sources that are inconvenient to incorporate into the perceptron directly. Experiments show that the cascaded model achieves improved...

In this paper, we report on experiments in which we used neural networks for statistical anomaly intrusion detection systems. The five types of neural networks that we studied were: Perceptron; Backpropagation; PerceptronBackpropagation-Hybrid; Fuzzy ARTMAP; and Radial-Based Function. We collected four separate data sets from different simulation scenarios, and these data sets were used to test...

In this paper, a neural fusion rule for fingerprint verification is presented. The person to be identified submits to the system her/his fingerprint and her/his identity. Multiple fingerprint matchers provide a set of verification scores, that are then fused by a perceptronbased method. The weights of such perceptron are explicitly optimised to increase the separation between genuine users and ...

We exhibit a duality between two perceptrons which allows us to compare the theoretical analysis of supervised and unsupervised learning tasks. The rst perceptron has one output and is asked to learn a classiication of p patterns. The second (dual) perceptron has p outputs and is asked to transmit as much information as possible on a distribution of inputs. We show in particular that the maximu...

We demonstrate how quantum computation can provide non-trivial improvements in the computational and statistical complexity of the perceptron model. We develop two quantum algorithms for perceptron learning. The first algorithm exploits quantum information processing to determine a separating hyperplane using a number of steps sublinear in the number of data points N , namely O( √ N). The secon...

Résumé— La simulation est souvent utilisée pour évaluer la pertinence d’un Programme Directeur de Production (PDP) ou pour en évaluer l’impact sur des scénarii d’ordonnancement détaillés de la fabrication. Dans ce cadre, nous nous proposons de réduire la complexité d’un modèle de simulation en exploitant un perceptron multicouches. Une phase primordiale lors de la modélisation d’un système à l’...

Predicting pronouns across languages from a language with less variation to one with much more is a hard task that requires many different types of information, such as morpho-syntactic information as well as lexical semantics and coreference. We assumed that continuous word spaces fed into a multi-layer perceptron enriched with morphological tags and coreference resolution would be able to cap...

Recent works about perceptron-based fusion of multiple fingerprint matchers showed the effectiveness of such approach in improving the performance of personal identity verification systems. However, to the best of our knowledge, no previous work investigated such fusion approach when stringent requirements in terms of verification errors are given, and the number of available samples for percep...

This paper proposes an approach to construct a better Semantic Perceptron Net (SPN) used for topic spotting. To accomplish this task a learning paradigm call: neural network ensembling is used. Applying this technique to the original structure of Semantic Perceptron Net a new system called GA-SPN (Genetic Algorithm based Semantic Perceptron Net) was developed. The new system uses a neural netwo...

Perceptron like large margin algorithms are introduced for the experiments with various margin selections. Compared to the previous perceptron reranking algorithms, the new algorithms use full pairwise samples and allow us to search for margins in a larger space. Our experimental results on the data set of (Collins, 2000) show that a perceptron like ordinal regression algorithm with uneven marg...