机器学习ppt1)资料

上传人:E**** 文档编号:99925539 上传时间:2019-09-21 格式:PPT 页数:33 大小:3.10MB
返回 下载 相关 举报
机器学习ppt1)资料_第1页
第1页 / 共33页
机器学习ppt1)资料_第2页
第2页 / 共33页
机器学习ppt1)资料_第3页
第3页 / 共33页
机器学习ppt1)资料_第4页
第4页 / 共33页
机器学习ppt1)资料_第5页
第5页 / 共33页
点击查看更多>>
资源描述

《机器学习ppt1)资料》由会员分享,可在线阅读,更多相关《机器学习ppt1)资料(33页珍藏版)》请在金锄头文库上搜索。

1、Introduction,Figure 1 Block diagram representation of nervous system.,Figure 2 The pyramidal cell.,Figure 3 Structural organization of levels in the brain.,Figure 4 Cytoarchitectural map of the cerebral cortex. The different areas are identified by the thickness of their layers and types of cells wi

2、thin them. Some of the key sensory areas are as follows: Motor cortex: motor strip, area 4; premotor area, area 6; frontal eye fields, area 8. Somatosensory cortex: areas 3, 1, and 2. Visual cortex: areas 17, 18, and 19. Auditory cortex: areas 41 and 42. (From A. Brodal, 1981; with permission of Oxf

3、ord University Press.),Figure 5 Nonlinear model of a neuron, labeled k.,Figure 6 Affine transformation produced by the presence of a bias; note that vk = bk at uk = 0.,Figure 7 Another nonlinear model of a neuron; wk0 accounts for the bias bk.,Figure 8 (a) Threshold function. (b) Sigmoid function fo

4、r varying slope parameter a.,Figure 9 lllustrating basic rules for the construction of signal-flow graphs.,Figure 10 Signal-flow graph of a neuron.,Figure 11 Architectural graph of a neuron.,Figure 12 Signal-flow graph of a single-loop feedback system.,Figure 13 (a) Signal-flow graph of a first-orde

5、r, infinite-duration impulse response (IIR) filter. (b) Feedforward approximation of part (a) of the figure, obtained by truncating Eq. (20).,Figure 14 Time response of Fig. 13 for three different values of feedforward weight w. (a) Stable. (b) Linear divergence. (c) Exponential divergence.,Figure 1

6、5 Feedforward network with a single layer of neurons.,Figure 16 Fully connected feedforward network with one hidden layer and one output layer.,Figure 17 Recurrent network with no self-feedback loops and no hidden neurons.,Figure 18 Recurrent network with hidden neurons.,Figure 19 Illustrating the r

7、elationship between inner product and Euclidean distance as measures of similarity between patterns.,Figure 20 Illustrating the combined use of a receptive field and weight sharing. All four hidden neurons share the same set of weights exactly for their six synaptic connections.,Figure 21 Block diag

8、ram of an invariant-feature-space type of system.,Figure 22 Autoregressive model of order 2: (a) tapped-delay-line model; (b) lattice-filter model. (The asterisk denotes complex conjugation.),Figure 23 Doppler-shift-invariant classifier of radar signals.,Figure 24 Block diagram of learning with a te

9、acher; the part of the figure printed in red constitutes a feedback loop.,Figure 25 Block diagram of reinforcement learning; the learning system and the environment are both inside the feedback loop.,Figure 26 Block diagram of unsupervised learning.,Figure 27 Inputoutput relation of pattern associat

10、or.,Figure 28 Illustration of the classical approach to pattern classification.,Figure 29 Block diagram of system identification: The neural network, doing the identification, is part of the feedback loop.,Figure 30 Block diagram of inverse system modeling. The neural network, acting as the inverse model, is part of the feedback loop.,Figure 31 Block diagram of feedback control system.,Figure 32 Block diagram of generalized sidelobe canceller.,

展开阅读全文
相关资源
正为您匹配相似的精品文档
相关搜索

最新文档


当前位置:首页 > 高等教育 > 大学课件

电脑版 |金锄头文库版权所有
经营许可证:蜀ICP备13022795号 | 川公网安备 51140202000112号