Results 241 to 250 of about 71,004,447 (289)

A New On-Line Learning Model

Neural Computation, 2001
We introduce a new supervised learning model that is a nonhomogeneous Markov process and investigate its properties. We are interested in conditions that ensure that the process converges to a “correct state,” which means that the system agrees with the teacher on every “question.” We prove a sufficient condition for almost sure convergence to a ...
openaire   +4 more sources

Fixed-weight on-line learning

IEEE Transactions on Neural Networks, 1999
Conventional artificial neural networks perform functional mappings from their input space to their output space. The synaptic weights encode information about the mapping in a manner analogous to long-term memory in biological systems. This paper presents a method of designing neural networks where recurrent signal loops store this knowledge in a ...
A. Steven Younger, Peter R. Conwell, Neil E. Cotter   +2 more
openaire   +2 more sources

A study on on-line learning of NNTrees

Proceedings of the International Joint Conference on Neural Networks, 2003., 2004
A neural network tree (NNTree) is a hybrid learning model with the overall structure being a decision tree (DT), and each nonterminal node containing a neural network (NN). Using NNTrees, it is possible to learn new knowledge online by adjusting the NNs in the nonterminal nodes. It is also possible to understand the learned knowledge online because the
Takeda Takaharu, Qiangfu Zhao, Yong Liu 0012   +2 more
openaire   +2 more sources

LineDL: Processing Images Line-by-Line With Deep Learning

IEEE Transactions on Image Processing, 2023
Although deep learning-based (DL-based) image processing algorithms have achieved superior performance, they are still difficult to apply on mobile devices (e.g., smartphones and cameras) due to the following reasons: 1) the high memory demand and 2) large model size.
Yujie Huang, Wenshu Chen, Liyuan Peng, Yuhao Liu 0001, Mingyu Wang 0001, Xiao-Ping (Steven) Zhang, Xiaoyang Zeng   +6 more
openaire   +2 more sources

On-line Learning on Temporal Manifolds

2016
We formulate an online learning algorithm that exploits the temporal smoothness of data evolving on trajectories in a temporal manifold. The learning agent builds an undirected graph whose nodes store the information provided by the data during the input evolution.
Maggini, Marco, Rossi, Alessandro
openaire   +3 more sources

Learning to Draw Sight Lines

International Journal of Computer Vision, 2019
In this paper, we are concerned with the task of gaze following. Given a scene (e.g. a girl playing soccer on the field) and a human subject’s head position, this task aims to infer where she is looking (e.g. at the soccer ball). An existing method adopts a saliency model conditioned on the head position.
Hao Zhao 0002, Ming Lu 0002, Anbang Yao, Yurong Chen 0001, Li Zhang 0023   +4 more
openaire   +1 more source

On-line EM reinforcement learning

Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks. IJCNN 2000. Neural Computing: New Challenges and Perspectives for the New Millennium, 2000
In this article, we propose a new reinforcement learning (RL) method for a system having continuous state and action spaces. Our RL method has an architecture like the actor-critic model. The critic tries to approximate the Q-function, which is the expected future return for the current state-action pair.
Junichiro Yoshimoto, Shin Ishii, Masa-aki Sato   +2 more
openaire   +2 more sources

Determinants of Learners' Self-Directed Learning and on-Line Learning Attitudes in on-Line Learning

Proceedings of the 15th International Conference on Computer Supported Education, 2023
Jing Li, Chi-Jen Chuang, Chi-Hui Wu
openaire   +1 more source

Learning curves of on-line and off-line training

1996
The performance of on-line training is compared with off-line or batch training using an unrealizable learning task. In naive off-line training this task shows a tendency to strong overfitting on the other hand its optimal training scheme is known.
openaire   +2 more sources

On-line learning in the Ising perceptron

Journal of Physics A: Mathematical and General, 2000
Summary: On-line learning of both binary and continuous rules in an Ising space is studied. Learning is achieved by using an artificial parameter, a weight vector \(\vec J\), which is constrained to the surface of a hypersphere (spherical constraint). In the case of a binary rule the generalization error decays to zero super-exponentially as \(\exp (-C\
openaire   +1 more source