GIUV2013-017
The main purpose of IDAL is the study and application of intelligent methods of data analysis for pattern recognition, with applications that struggle with prediction, classification or trend determination. Its members apply classic statistical methods and automatic learning techniques to large databases: statistical hypothesis testing, linear models, feature selection and extraction, neural networks, clustering algorithms, decision trees, support vector machines, probabilistic graphical models, manifold visualization, fuzzy logic, reinforcement learning, etc. The ultimate goal of the application of these methods is to generate mathematical models which enable the optimization of processes and resources, as well as to reach the optimal decision making stage. A clear example of this is the area of health, where IDAL has developed clinical decision support application based on data analysis. These applications make it possible to improve the patient¿s quality of life (establishing optimal clinical guidelines) while reducing healthcare costs. Complementing this knowledge, the group has extensive experience in signal processing (spectral analysis, digital filter, adaptive process,...The main purpose of IDAL is the study and application of intelligent methods of data analysis for pattern recognition, with applications that struggle with prediction, classification or trend determination. Its members apply classic statistical methods and automatic learning techniques to large databases: statistical hypothesis testing, linear models, feature selection and extraction, neural networks, clustering algorithms, decision trees, support vector machines, probabilistic graphical models, manifold visualization, fuzzy logic, reinforcement learning, etc. The ultimate goal of the application of these methods is to generate mathematical models which enable the optimization of processes and resources, as well as to reach the optimal decision making stage. A clear example of this is the area of health, where IDAL has developed clinical decision support application based on data analysis. These applications make it possible to improve the patient¿s quality of life (establishing optimal clinical guidelines) while reducing healthcare costs. Complementing this knowledge, the group has extensive experience in signal processing (spectral analysis, digital filter, adaptive process, etc.) due to their work of over 10 years in biosignal processing (mainly ECG and EEG). With all this background, IDAL is able to analyse a wide range of data and signals. This fact is backed up by the large number of both private and public contracts it has developed in different areas of knowledge. Furthermore, most of the practical work carried out has been displayed in important scientific publications with high impact parameters and in a large number of communications to international congresses within the area of data analysis. Among the developed applications, (outside the health area already mentioned) are the following, i.a: web recommendations, models for optimal incentive management to gain customer loyalty, measurement-based shoe recommendations, and other data analysis consultancy works. In addition to its practical work IDAL, it develops new data analysis algorithms improving the performance of the existing ones. This research work is also reflected in a wide dissemination in the form of different publications in journals of impact and in congresses of data analysis relevant to the scientific community.
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- Mineria de datos avanzada
- Extraccion de conocimiento de grandes bases de datos
- Aplicacion de sistemas expertos en aplicaciones reales
- Desarrollo de nuevos algoritmos de analisis de datos
- Big data
- Intelligent data analysis.Application of automatic learning techniques for problems with prediction, classification and recognition of patterns or trends.
- Process optimisation.Development of reinforcement learning models and dynamic programming for cost reduction, the improvement of important parameters and the increase of efficiency.
- Recommender system.Development of product recommendation engines based on the characteristics of the customer and management of personalised promotions.
- Signal capture and processing.Development of equipment and algorithms custom-made for their aqcuisition and signal processing.
- Big Data.Large database analysis in which there are three characteristics that make them special: growth velocity, variety in the data classes and volume.
- Natural Language Processing.Extraction of structured information and knowledge from the analysis of free texts and a priori unstructured information.
- Quantum machine learning.Use of formalism of quantum mechanics to improve the performance of machine learning algorithms. Use of machine learning for the description and extraction of quantum phenomena knowledge.
| Name | Nature of participation | Entity | Description |
|---|---|---|---|
| JOSE RAFAEL MAGDALENA BENEDICTO | Director | Universitat de València | |
| Research team | |||
| JOAN VILA FRANCES | Member | Universitat de València | |
| MARCELINO MARTINEZ SOBER | Member | Universitat de València | |
| EMILIO SORIA OLIVAS | Member | Universitat de València | |
| ANTONIO JOSE SERRANO LOPEZ | Member | Universitat de València | |
| JOSE DAVID MARTIN GUERRERO | Member | Universitat de València | |
| FERNANDO MATEO JIMENEZ | Member | Universitat de València | |
| JUAN JOSE CARRASCO FERNANDEZ | Member | Universitat de València | |
| JUAN GOMEZ SANCHIS | Member | Universitat de València | |
| YOLANDA VIVES GILABERT | Member | Universitat de València | |
| OSCAR JOSE PELLICER VALERO | Member | Universitat de València | |
| RICARDO SANZ DIAZ | Member | Universitat de València | |
| JOSE ENRIQUE VILA GISBERT | Collaborator | Universitat de València | |
| ANDREA BONETTI | Collaborator | Universitat de València | |
| ALEJANDRO DIONIS ROS | Collaborator | Universitat de València | |
| JUAN FRANCISCO RODRÍGUEZ HERNÁNDEZ | Collaborator | University of Sidney (Australia) | researcher |
| SONIA PÉREZ DÍAZ | Collaborator | Universidad de Alcalá | full university professor |
| EMILIO BERNARDO FERNANDEZ VARGAS | Collaborator | Universitat de València | |
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- Electronic Engineering
- DEEP LEARNING
- BIG DATA
- HADOOP
- MAPREDUCE
- DICCIONARIO
- CORPUS
- INFORMACIÓN CUÁNTICA
- TECNOLOGÍA CUÁNTICA
- Predicción; Clasificación automática; Agrupamientos; Extracción de conocimiento; Reconocimiento de patrones o tendencias; Visualización; Deep Learning
- Sensores; Acondicionadores de señal; Procesado digital de señales; Análisis espectral; Algoritmos adaptativos
- Diccionario; Corpus; N-gramas
- q-bit; información cuántica; tecnología cuántica
- REINFORCEMENT LEARNING
- DYNAMIC PROGRAMMING
- CRITICAL PATH IDENTIFICATION
- EFFICENCY
- PERSONALISATION OF PROMOTIONS
- PRODUCTS RECOMMENDER
- GROUPING OF CUSTOMERS
- AUTOMATIC CLASSIFICATION
- GROUPING
- KNOWLEDGE EXTRACTION
- PATTERN OR TREND RECOGNITION
- VISUALISATION
- IDENTIFICATION OF DEMANDS
- SIGNAL CONDITIONERS
- DIGITAL SIGNAL PROCESSING
- SPECTRAL ANALYSIS
- ADAPTIVE ALGORITHMS
