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- The University of TokyoJP41 papers
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8 papers · 1 filter
An Object Detection by using Adaptive Structural Learning of Deep Belief Network
Shin Kamada, Takumi Ichimura
Deep learning forms a hierarchical network structure for representation of multiple input features. The adaptive structural learning method of Deep Belief Network (DBN) can realize…
Adaptive Structural Learning of Deep Belief Network for Medical Examination Data and Its Knowledge Extraction by using C4.5
Shin Kamada, Takumi Ichimura, Toshihide Harada
Deep Learning has a hierarchical network architecture to represent the complicated feature of input patterns. The adaptive structural learning method of Deep Belief Network (DBN) h…
Knowledge Extracted from Recurrent Deep Belief Network for Real Time Deterministic Control
Shin Kamada, Takumi Ichimura
Recently, the market on deep learning including not only software but also hardware is developing rapidly. Big data is collected through IoT devices and the industry world will ana…
Adaptive Learning Method of Recurrent Temporal Deep Belief Network to Analyze Time Series Data
Takumi Ichimura, Shin Kamada
Deep Learning has the hierarchical network architecture to represent the complicated features of input patterns. Such architecture is well known to represent higher learning capabi…
Shortening Time Required for Adaptive Structural Learning Method of Deep Belief Network with Multi-Modal Data Arrangement
Shin Kamada, Takumi Ichimura
Recently, Deep Learning has been applied in the techniques of artificial intelligence. Especially, Deep Learning performed good results in the field of image recognition. Most new…
Fine Tuning Method by using Knowledge Acquisition from Deep Belief Network
Shin Kamada, Takumi Ichimura
We developed an adaptive structure learning method of Restricted Boltzmann Machine (RBM) which can generate/annihilate neurons by self-organizing learning method according to input…