Fast Incremental Learning Strategy Driven by Confusion Reject for Online Handwriting Recognition

International audienceIn this paper, we present a new incremental learning strategy for handwritten character recognition systems. This learning strategy enables the recognition system to learn “rapidly” any new character from very few examples. The presented strategy is driven by a confusion detection mechanism in order to control the learning process. Artificial characters generation techniques are used to overcome the problem of lack of learning data when introducing a new character from unseen class. The results show that a good recognition rate (about 90%) is achieved after only 5 learning examples. Moreover, the rate quickly rises to 94% after 10 examples, and approximately 97% after 30 examples. A reduction of error of 40% is obtained by using the artificial characters generation techniques

An improved dental composite with potent antibacterial function

A new BisGMA-based antibacterial dental composite has been formulated and evaluated. Compressive strength and bacterial viability were utilized to evaluate the formed composites. It was found that the new composite exhibited a significantly enhanced antibacterial function along with improved mechanical and physical properties. The bromine-containing derivative-modified composite was more potent in antibacterial activity than the chlorine-containing composite. The modified composites also exhibited an increase of 30–53% in compressive yield strength, 15–30% in compressive modulus, 15–33% in diametral tensile strength and 6–20% in flexural strength, and a decrease of 57–76% in bacterial viability, 23–37% in water sorption, 8–15% in shrinkage, 8–13% in compressive strength, and similar degree of conversion, than unmodified composite. It appears that this experimental composite may possibly be introduced to dental clinics as an attractive dental restorative due to its improved properties as well as enhanced antibacterial function

Systèmes d'inférence floue auto-évolutifs : apprentissage incrémental pour la reconnaissance de gestes manuscrits

International audienceNous présentons dans ce papier une nouvelle méthode pour la conception de moteurs de reconnaissance de gestes manuscrits personnalisables et auto-évolutifs, c'est-à-dire capables de s'adapter au style d'écriture et aux habitudes de chacun, sans toutefois nécessiter de période d'apprentissage fastidieuse. Nous utilisons une approche d'apprentissage incrémental de classifieurs basés sur les systèmes d'inférence floue de type Takagi-Sugeno. Cette approche comprend d'une part, une adaptation des paramètres linéaires associés aux conclusions des règles en utilisant la méthode des moindres carrés récursifs, et d'autre part, un apprentissage incrémental des prémisses de ces règles afin de modifier les fonctions d'appartenance suivant l'évolution de la densité des données dans l'espace de classification

Fast Online Incremental Learning with Few Examples For Online Handwritten Character Recognition.

International audienceAn incremental learning strategy for handwritten character recognition is proposed in this paper. The strategy is online and fast, in the sense that any new character class can be instantly learned by the system. The proposed strategy aims at overcoming the problem of lack of training data when introducing a new character class. Synthetic handwritten characters generation is used for this purpose. Our approach uses a Fuzzy Inference System (FIS) as a classifier. Results have shown that a good recognition rate (about 90%) can be achieved using only 3 training examples. And such rate rapidly improves reaching 96% for 10 examples, and 97% for 30 ones