Abstract-How to learn new knowledge without forgetting old knowledge is a key issue in designing an incremental-learning neural network. In this paper, we present a new incremental learning method for pattern recognition, called the “incremental backpropagation learning network, ” which employs bounded weight modification and structural adaptation learning rules and applies initial knowledge to constrain the learning process. The viability of this approach is demonstrated for classification problems including the iris and the promoter domains. 1
L'apprentissage incrémental propose un nouveau paradigme d'apprentissage pour les réseaux de neurone...
[[abstract]]In this paper, we applied the concepts of minimizing weight sensitivity cost and trainin...
AbstractArtificial neural network (ANN) has wide applications such as data processing and classifica...
[[abstract]]How to learn new knowledge without forgetting old knowledge is a key issue in designing ...
[[abstract]]How to learn new knowledge without forgetting old knowledge is a key issue in designing ...
We present a new type of constructive algorithm for incremental learning. The algorithm overcomes ma...
Up until the recent past, the power of multi layer feed forward artificial neural networks has been ...
Neural networks are generally exposed to a dynamic environment where the training patterns or the in...
M.Ing. (Electrical And Electronic Engineering)This dissertation describes the development of a syste...
A classi er for discrete-valued variable classi cation problems is presented. The system utilizes an...
We present a new incremental procedure for supervised learning with noisy data. Each step consists i...
Incremental learning introduces a new learning paradigm for artificial neural networks. It aims at d...
We propose a novel neural network for incremental learning tasks where networks are required to lear...
A realtime online learning system with capacity limits needs to gradually forget old information in ...
Backpropagation is a supervised learning algorithm for training multi-layer neural networks for func...
L'apprentissage incrémental propose un nouveau paradigme d'apprentissage pour les réseaux de neurone...
[[abstract]]In this paper, we applied the concepts of minimizing weight sensitivity cost and trainin...
AbstractArtificial neural network (ANN) has wide applications such as data processing and classifica...
[[abstract]]How to learn new knowledge without forgetting old knowledge is a key issue in designing ...
[[abstract]]How to learn new knowledge without forgetting old knowledge is a key issue in designing ...
We present a new type of constructive algorithm for incremental learning. The algorithm overcomes ma...
Up until the recent past, the power of multi layer feed forward artificial neural networks has been ...
Neural networks are generally exposed to a dynamic environment where the training patterns or the in...
M.Ing. (Electrical And Electronic Engineering)This dissertation describes the development of a syste...
A classi er for discrete-valued variable classi cation problems is presented. The system utilizes an...
We present a new incremental procedure for supervised learning with noisy data. Each step consists i...
Incremental learning introduces a new learning paradigm for artificial neural networks. It aims at d...
We propose a novel neural network for incremental learning tasks where networks are required to lear...
A realtime online learning system with capacity limits needs to gradually forget old information in ...
Backpropagation is a supervised learning algorithm for training multi-layer neural networks for func...
L'apprentissage incrémental propose un nouveau paradigme d'apprentissage pour les réseaux de neurone...
[[abstract]]In this paper, we applied the concepts of minimizing weight sensitivity cost and trainin...
AbstractArtificial neural network (ANN) has wide applications such as data processing and classifica...