Deep belief network

노트

위키데이터

• ID : Q16954980

말뭉치

1. Hence, we choose MATLAB to implement DBN.
2. An important thing to keep in mind is that implementing a Deep Belief Network demands training each layer of RBM.
3. The greedy learning algorithm is used to train the entire Deep Belief Network.
4. When trained on a set of examples without supervision, a DBN can learn to probabilistically reconstruct its inputs.
5. In this work, a novel deep belief network (DBN) hyperspectral image classification method based on multivariate optical sensors and stacked by restricted Boltzmann machines is proposed.
6. We introduced the DBN framework to classify spatial hyperspectral sensor data on the basis of DBN.
7. After unsupervised pretraining and supervised fine-tuning, the DBN model could successfully learn features.
8. A graphical model corresponding to a deep belief network.
9. This paper presents the first proof-of-concept of how to transform a DBN model trained offline into the event-based domain.
10. In addition we present an event-based DBN architecture that can associate visual and auditory inputs, and combine multiple uncertain cues from different sensory modalities in a near-optimal way.
11. The visible layers of RBMs at the bottom of a DBN are clamped to the actual inputs when data is presented.
12. When RBMs are stacked to form a DBN, the hidden layer of the lower RBM becomes the visible layer of the next higher RBM.
13. Except for the first and last layers, each level in a DBN serves a dual role function: it’s the hidden layer for the nodes that came before and the visible (output) layer for the nodes that come next.
14. A deep neural network pre-trained by a deep belief network (DBN-DNN).
15. The sequence of steps to create a DBN using greedy-layer-wise pre-training and convert it to a DBN-DNN.
16. All these weights are combined in one structure called a DBN.
17. Finally, a softmax layer is added to the top of the DBN, and all the layers undergo supervised fine-tuned as one DNN.
18. As you have pointed out a deep belief network has undirected connections between some layers.
19. The undirected layers in the DBN are called Restricted Boltzmann Machines.
20. In this paper, the deep belief network algorithm in the theory of deep learning is introduced to extract the in-depth features of the imaging spectral image data.
21. Then, a deep belief network will be formed by superimposed multiple Restricted Boltzmann Machines and training the model parameters by using the greedy algorithm layer by layer.
22. The conventional dimension reduction method and deep belief network are compared to extract hyperspectral image information, and the robustness and separability of abstract features are considered.
23. The deep belief network is a superposition of a multilayer of Restricted Boltzmann Machines, which can extract the indepth features of the original data.
24. The adaptive structural learning method of Deep Belief Network (DBN) can reach the high classification capability while searching the optimal network structure during the training.
25. In this paper, the proposed adaptive structural learning of DBN (Adaptive DBN) was applied to the comprehensive medical examination data for cancer prediction.
26. Moreover, the explicit knowledge that makes the inference process of the trained DBN is required in deep learning.
27. The binary patterns of activated neurons for given input in RBM and the hierarchical structure of DBN can represent the relation between input and output signals.
28. This paper proposes a novel approach based on sparse deep belief network (DBN) for structural damage identification with uncertain and limited data.
29. DBN is chosen to train the generated data sets and identify structural damages.
30. Restricted Boltzmann Machines (RBMs) are used as building blocks to composite a DBN.
31. Finally, a top level RBM combines these DBNs into a single network we call the Multiresolution Deep Belief Network (MrDBN).
32. This paper proposes an intrusion detection technique based on DBN (Deep Belief Network) to classify four intrusion classes and one normal class using KDD-99 dataset.
33. Definition - What does Deep Belief Network (DBN) mean?