Papers by Francisco Zamora-Martinez
2015 13th International Conference on Document Analysis and Recognition (ICDAR), 2015
A Josepa por apoyarme en estos últimos años de escritura. A mis padres, Natividad y Crescencio, y... more A Josepa por apoyarme en estos últimos años de escritura. A mis padres, Natividad y Crescencio, y hermanos, José y Nati, por creer en mí.
Behaviour-Based Clustering of Neural Networks (9781599048499): María José Castro-Bleda, Slavador ... more Behaviour-Based Clustering of Neural Networks (9781599048499): María José Castro-Bleda, Slavador España-Boquera, Francisco Zamora-Martínez: Book Chapters.
Sensors, 2015
Time series forecasting is an important predictive methodology which can be applied to a wide ran... more Time series forecasting is an important predictive methodology which can be applied to a wide range of problems. Particularly, forecasting the indoor temperature permits an improved utilization of the HVAC (Heating, Ventilating and Air Conditioning) systems in a home and thus a better energy efficiency. With such purpose the paper describes how to implement an Artificial Neural Network (ANN) algorithm in a low cost system-on-chip to develop an autonomous intelligent wireless sensor network. The present paper uses a Wireless Sensor Networks (WSN) to monitor and forecast the indoor temperature in a smart home, based on low resources and cost microcontroller technology as the 8051MCU. An on-line learning approach, based on Back-Propagation (BP) algorithm for ANNs, has been developed for real-time time series learning. It performs the model training with every new data that arrive to the system, without saving enormous quantities of data to create a historical database as usual, i.e., without previous knowledge. Consequently to validate the approach a simulation study through a Bayesian baseline model have been tested in order to compare with a database of a real application aiming to see the performance and accuracy. The core of the paper is a new algorithm, based on the BP one, which has been described in detail, and the challenge was how to implement a computational demanding algorithm in a simple architecture with very few hardware resources.
Lecture Notes in Computer Science, 2010
The recognition performance of current automatic offline handwriting transcription systems is far... more The recognition performance of current automatic offline handwriting transcription systems is far from being perfect. This is the reason why there is a growing interest in assisted transcription systems, which are more efficient than correcting by hand an automatic transcription. A recent approach to interactive transcription involves multimodal recognition, where the user can supply an online transcription of some of the words. In this paper, a description of the bimodal engine, which entered the "Bi-modal Handwritten Text Recognition" contest organized during the 2010 ICPR, is presented. The proposed recognition system uses Hidden Markov Models hybridized with neural networks (HMM/ANN) for both offline and online input. The N -best word hypothesis scores for both the offline and the online samples are combined using a log-linear combination, achieving very satisfying results.
ABSTRACT Artificial neural networks have proved to be good at time series forecasting problems, b... more ABSTRACT Artificial neural networks have proved to be good at time series forecasting problems, being widely studied at literature. Traditionally, shallow architectures were used due to convergence problems when dealing with deep models. Recent research findings enable deep architectures training, opening a new interesting research area called deep learning. This paper presents a study of deep learning techniques applied to time-series forecasting in a real indoor temperature forecasting task, studying performance due to different hyper-parameter configurations. When using deep models, better generalization performance at test set and an over-fitting reduction has been observed.
ABSTRACT This paper describes the system presented for the English-Spanish translation task by th... more ABSTRACT This paper describes the system presented for the English-Spanish translation task by the collaboration between CEU-UCH and UPV for 2011 WMT. A comparison of independent phrase-based translation models interpolation for each available training corpora were tested, giving an improvement of 0.4 BLEU points over the baseline. Output N-best lists were rescored via a target Neural Network Language Model. An improvement of one BLEU point over the baseline was obtained adding the two features, giving 31.5 BLEU and 57.9 TER for the primary system, computed over lowercased and detokenized outputs. The system was positioned second in the final ranking.
The aim of this work is to improve the performance of off-line handwritten text recognition syste... more The aim of this work is to improve the performance of off-line handwritten text recognition systems based on hidden Markov models (HMM) and hybrid Markov models with neural networks (HMM/ANN). In order to study the systems without the influence of the language model, an isolated word recognition task has been performed. The analysis of the influence of word lengths on
The integration of a cache memory into a connectionist language model is proposed in this paper. ... more The integration of a cache memory into a connectionist language model is proposed in this paper. The model captures long term dependencies of both words and concepts and is particularly useful for Spoken Language Understanding tasks. Experiments conducted on a human-machine telephone dialog corpus are reported, and an increase in performance is observed when features of previous turns are taken into account for predicting the concepts expressed in a user turn. In terms of Concept Error Rate we obtained a statistically significant improvement of 3.2 points over our baseline (10% relative improvement) on the French Media corpus.
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Papers by Francisco Zamora-Martinez