Auto Topic: kernel
auto_kernel | topic
Coverage Score
1
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46
Mentioned Docs
1
Required Dimensions
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Covered Dimensions
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Keywords
kernel
Relations
| Source | Type | Target | W |
|---|---|---|---|
| Auto Topic: kernel | CO_OCCURS | Propositional Logic | 11 |
| Auto Topic: kernel | CO_OCCURS | Auto Topic: stride | 8 |
| Auto Topic: convolution | CO_OCCURS | Auto Topic: kernel | 8 |
| Auto Topic: kernel | CO_OCCURS | Constraint Satisfaction Problem | 6 |
| Auto Topic: kernel | CO_OCCURS | Logical Agents | 6 |
| Auto Topic: kernel | CO_OCCURS | Auto Topic: margin | 6 |
| Auto Topic: kernel | CO_OCCURS | Auto Topic: pixels | 6 |
| Auto Topic: dimension | CO_OCCURS | Auto Topic: kernel | 6 |
| Auto Topic: kernel | CO_OCCURS | Inference | 5 |
| Auto Topic: kernel | CO_OCCURS | Auto Topic: separator | 5 |
| Auto Topic: convolutional | CO_OCCURS | Auto Topic: kernel | 5 |
| Auto Topic: activation | CO_OCCURS | Auto Topic: kernel | 5 |
| Auto Topic: kernel | CO_OCCURS | Auto Topic: self | 4 |
| Auto Topic: cnn | CO_OCCURS | Auto Topic: kernel | 4 |
| Auto Topic: boundary | CO_OCCURS | Auto Topic: kernel | 3 |
| Auto Topic: cnns | CO_OCCURS | Auto Topic: kernel | 3 |
Evidence Chunks
| Source | Confidence | Mentions | Snippet |
|---|---|---|---|
textbook Artificial-Intelligence-A-Modern-Approach-4th-Edition.pdf | 0.67 | 10 | · xk in Equation (19.10) with a kernel function K(x j,xk). Thus, we can learn in the higher-dimensional space, but we compute only kernel functions rather than the full list of features for each data point. The next step is to see that there’s nothing special about the kernelK(x ... |
textbook Artificial-Intelligence-A-Modern-Approach-4th-Edition.pdf | 0.65 | 6 | ... The decrease in weight over distance is typically gradual, not sudden. We decide how much to weight each example with a function known as a kernel, whose Kernel input is a distance between the query point and the example. A kernel function K is a de- creasing function of distance ... |
textbook Artificial-Intelligence-A-Modern-Approach-4th-Edition.pdf | 0.63 | 5 | ... 34, 1101 Kenley, C. R., 473, 1112 Kephart, J. O., 79, 1101 Kepler, J., 1027 Keras (machine learning software), 738, 1072 Kern, C., 48, 1104 kernel (in neural networks), 811 kernel (in regression), 709 kernel function, 712, 787 kernelization, 714 kernel machine, 710–714, 735 kerne ... |
textbook Artificial-Intelligence-A-Modern-Approach-4th-Edition.pdf | 0.61 | 4 | en the kernelk and a snippet of x centered on xi with width l. The process is illustrated in Figure 22.4 for a kernel vector [+1, −1, +1], which detects a darker point in the 1D image. (The 2D version might detect a darker line.) Notice that in this example the pixels on which th ... |
textbook Artificial-Intelligence-A-Modern-Approach-4th-Edition.pdf | 0.59 | 3 | st be finite—and if we choose to make the integral 1, certain calculations are easier. Figure 19.20(d) was generated with a quadratic kernel, K(d) = max(0,1 − (2|d|/w)2), with kernel width w =10. Other shapes, such as Gaussians, are also used. Typically, the width matters more tha ... |
textbook Artificial-Intelligence-A-Modern-Approach-4th-Edition.pdf | 0.59 | 3 | ... 12), a little bit of algebra shows that F(x j) · F(xk) = (x j · xk)2. (That’s why the √ 2 is in f3.) The expression (x j · xk)2 is called a kernel function,13 andKernel function is usually written as K(x j,xk). The kernel function can be applied to pairs of input data to 11 The f ... |
textbook Artificial-Intelligence-A-Modern-Approach-4th-Edition.pdf | 0.59 | 3 | D mapping illustrates the idea better. 13 This usage of “kernel function” is slightly different from the kernels in locally weighted regression. Some SVM kernels are distance metrics, but not all are. Section 19.7 Nonparametric Models 713 -1.5 -1 -0.5 0 0.5 1 1.5 -1.5 -1 -0.5 0 0 ... |
textbook Artificial-Intelligence-A-Modern-Approach-4th-Edition.pdf | 0.59 | 3 | butes (Berlin et al., 2015). The ideas behind kernel machines come from Aizerman et al. (1964) (who also in- troduced the kernel trick), but the full development of the theory is due to Vapnik and his 736 Chapter 19 Learning from Examples colleagues (Boser et al., 1992). SVMs wer ... |
textbook Artificial-Intelligence-A-Modern-Approach-4th-Edition.pdf | 0.59 | 3 | ... ights that are replicated across the units in each layer. A pattern of weights that is replicated across multiple local regions is called a kernel and the process of applying the kernel to the pixels of the image (or to spatially Kernel organized units in a subsequent layer) is c ... |
textbook Artificial-Intelligence-A-Modern-Approach-4th-Edition.pdf | 0.59 | 3 | 4 ) . (22.9) In this weight matrix, the kernel appears in each row, shifted according to the stride relative to the previous row, One wouldn’t necessarily construct the weight matrix explicitly—it is Section 22.3 Convolutional Networks 813 Figure 22.5 The first two layers of a CNN ... |
textbook Artificial-Intelligence-A-Modern-Approach-4th-Edition.pdf | 0.59 | 3 | ... nal array of hidden units, where the third dimension is of size d. It is im- portant to organize the hidden layer this way, so that all the kernel outputs from a particular image location stay associated with that location. Unlike the spatial dimensions of the image, however, thi ... |
textbook Artificial-Intelligence-A-Modern-Approach-4th-Edition.pdf | 0.57 | 2 | ... n with some of the basic concepts for analyzing Markov chains in general. Any such chain is defined by its initial state and its transition kernel k(x → x′)—the probabilityTransition kernel of a transition to state x′ starting from state x. Now suppose that we run the Markov chain ... |
textbook Artificial-Intelligence-A-Modern-Approach-4th-Edition.pdf | 0.57 | 2 | lityTransition kernel of a transition to state x′ starting from state x. Now suppose that we run the Markov chain for t steps, and let πt(x) be the probability that the system is in state x at time t. Similarly, letπt+1(x′) be the probability of being in state x′ at time t + 1. G ... |
textbook Artificial-Intelligence-A-Modern-Approach-4th-Edition.pdf | 0.57 | 2 | ... ach regression problem will be easier to solve, because it involves only the examples with nonzero weight—the examples that are within the kernel width of the query. When kernel widths are small, this may be just a few points. Most nonparametric models have the advantage that it ... |
textbook Artificial-Intelligence-A-Modern-Approach-4th-Edition.pdf | 0.57 | 2 | ... eSoft margin decision boundary, but assigns them a penalty proportional to the distance required to move them back to the correct side. The kernel method can be applied not only with learning algorithms that find optimal linear separators, but also with any other algorithm that ca ... |
textbook Artificial-Intelligence-A-Modern-Approach-4th-Edition.pdf | 0.57 | 2 | ... The technique is particularly useful for genomic data, where each record has millions of attributes (Berlin et al., 2015). The ideas behind kernel machines come from Aizerman et al. (1964) (who also in- troduced the kernel trick), but the full development of the theory is due to ... |
textbook Artificial-Intelligence-A-Modern-Approach-4th-Edition.pdf | 0.57 | 2 | ... clear that (b) is about right, while (a) is too spiky (k is too small) and (c) is too smooth (k is too big). Another possibility is to use kernel functions, as we did for locally weighted regression. To apply a kernel model to density estimation, assume that each data point gene ... |
textbook Artificial-Intelligence-A-Modern-Approach-4th-Edition.pdf | 0.57 | 2 | ... r 22 Deep Learning 5 6 6 2 5 6 5 5 9 4 +1 –1 +1 +1 –1 +1 +1 –1 +1 Figure 22.4 An example of a one-dimensional convolution operation with a kernel of size l =3 and a stride s =2. The peak response is centered on the darker (lower intensity) input pixel. The results would usually b ... |