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Why Machines Learn > 7 - The Great Kernel Rope Trick > Flashcards

7 - The Great Kernel Rope Trick Flashcards

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1
Q

Who was Bernhard Boser?

A

A member of the technical staff at AT&T Bell Labs working on artificial neural networks.

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2
Q

What position was Bernhard Boser offered at the University of California?

A

A position at the University of California, Berkeley.

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3
Q

Who is Vladimir Vapnik?

A

An eminent Russian mathematician and expert in statistics and machine learning.

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4
Q

What algorithm did Vapnik ask Boser to implement?

A

Methods for Constructing an Optimal Separating Hyperplane.

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5
Q

Define separating hyperplane.

A

A linear boundary between two regions of coordinate space.

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6
Q

What does the perceptron algorithm do?

A

Finds a hyperplane to separate labeled data points.

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7
Q

True or False: There exists an infinity of separating hyperplanes for a linearly separable dataset.

A

True.

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8
Q

What is the problem with the perceptron algorithm when classifying new data points?

A

It may misclassify new points based on the previously found hyperplane.

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9
Q

What does Vapnik’s method aim to find?

A

An optimal hyperplane that minimizes classification errors.

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10
Q

What does the weight vector ‘w’ characterize?

A

The hyperplane and is perpendicular to it.

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11
Q

What is the bias ‘b’ in the context of hyperplanes?

A

The offset of the hyperplane from the origin.

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12
Q

What is the margin rule?

A

Ensures that points on either side of the hyperplane can only get so close.

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13
Q

What is a constrained optimization problem?

A

An optimization problem that must satisfy certain constraints.

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14
Q

Who devised a solution for constrained optimization problems?

A

Joseph-Louis Lagrange.

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15
Q

What does Lagrange’s insight involve?

A

The gradients of two functions being scalar multiples of each other.

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16
Q

What is the equation of the constraint used in the mining metaphor?

A

x² + y² = r².

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17
Q

What is the significance of contour lines in optimization?

A

They represent paths along the surface at the same height.

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18
Q

What does the gradient of a function represent?

A

The direction of steepest ascent.

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19
Q

Fill in the blank: The function f(x, y) = xy + 30 has a _______ point.

A

saddle

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20
Q

What is the primary goal of Vapnik’s algorithm?

A

To find the hyperplane that maximizes margins between data clusters.

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21
Q

How is the weight vector ‘w’ related to the hyperplane?

A

It is perpendicular to the hyperplane.

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22
Q

What does the function to be minimized represent in Vapnik’s algorithm?

A

The magnitude of the weight vector.

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23
Q

What happens when you find a hyperplane using Vapnik’s method?

A

It is more likely to classify new data points correctly.

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24
Q

What is the gradient of a function in 3D space?

A

A two-dimensional vector consisting of the partial derivatives with respect to x and y.

The gradient represents the direction and rate of the steepest ascent of the function.

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25
What does Lagrange's method state about the gradients of two functions?
∇ f ( x , y ) = λ∇ g ( x , y ), where λ is a scalar multiple. ## Footnote This relationship is used in constrained optimization problems.
26
What equations arise from Lagrange's method when optimizing functions?
y = λ^2 x and x = λ^2 y. ## Footnote These equations are derived from setting the gradients equal to each other.
27
What is the constraining equation in the optimization problem discussed?
x^2 + y^2 = 4. ## Footnote This equation represents a constraint on the values of x and y.
28
What is the Lagrange function in constrained optimization?
L ( x , λ ) = f ( x ) - λg ( x ). ## Footnote It combines the objective function and the constraint.
29
What does the gradient of the Lagrange function equal at extrema?
∇ L ( x , λ ) = 0. ## Footnote This condition indicates that we are at a critical point.
30
What is the significance of the Lagrange multipliers?
They help solve constrained optimization problems. ## Footnote Each multiplier corresponds to a constraint in the optimization problem.
31
What is a support vector in the context of optimization?
Data points that lie on the margins and help define the optimal separating hyperplane. ## Footnote Only these points contribute to the calculation of the decision boundary.
32
What is the decision rule for classifying a new data point u?
The label is determined by the dot product of u with each support vector. ## Footnote It indicates whether u is classified as +1 or -1.
33
True or False: The optimal separating hyperplane depends on all data points.
False. ## Footnote It depends only on the support vectors.
34
What happens when data is projected into higher dimensions?
It may become linearly separable. ## Footnote This technique is used to find a hyperplane in cases where data is not linearly separable in lower dimensions.
35
What are two major concerns when projecting data into higher-dimensional spaces?
* Computational costs of dot products * Managing infinite-dimensional spaces. ## Footnote High-dimensional projections can lead to computational challenges.
36
What solution did Isabelle Guyon propose to address the computation of dot products in higher-dimensional spaces?
A method that bypasses the need to compute dot products directly. ## Footnote This insight contributed to the development of effective ML algorithms.
37
What is the significance of the algorithm developed by Vapnik in 1964?
It allowed for finding nonlinear boundaries in classification tasks. ## Footnote This work laid the groundwork for modern support vector machines.
38
Fill in the blank: The equation for the weight vector is given by __________.
w = Σ α_i y_i x_i. ## Footnote Each α_i is a Lagrange multiplier associated with the data point (x_i, y_i).
39
What were two key ideas encountered by Isabelle Guyon during her Ph.D. that influenced her later work?
* Optimal margin classifiers * Memory storage in Hopfield networks. ## Footnote These ideas shaped her understanding of classification algorithms.
40
What does the term 'optimal margin classifier' refer to?
A classifier that finds the best linear boundary to separate different classes. ## Footnote This concept focuses on maximizing the margin between classes.
41
What does the method of projecting data into higher dimensions help achieve?
It facilitates finding a linear separating hyperplane for previously inseparable data. ## Footnote This is essential for classification tasks in machine learning.
42
What is the role of the bias term b in the context of hyperplanes?
It helps to determine the position of the hyperplane in the feature space. ## Footnote The bias shifts the hyperplane away from the origin.
43
What is the main challenge when projecting data into higher dimensions?
Finding a linearly separating hyperplane becomes computationally intractable due to the large dimensionality.
44
What did Aizerman, Braverman, and Rozonoer demonstrate in their 1964 paper?
They showed how to reformulate the perceptron algorithm to classify data points based on the dot product of a data point with every other data point in the training dataset.
45
What is the mapping used to project data from 2D to 3D?
x j → φ ( x j )
46
What is the purpose of the kernel function K?
To compute the dot product of higher-dimensional vectors without actually transforming the lower-dimensional vectors.
47
True or False: The kernel trick allows calculations in high-dimensional space without ever explicitly forming high-dimensional vectors.
True
48
What is the polynomial kernel's general form?
K ( x, y ) = ( c + x.y ) d
49
What happens when constants c and d are set to 0 and 2 in the polynomial kernel?
It results in K ( x, y ) = ( x.y ) 2.
50
Fill in the blank: The method of using a kernel function to compute dot products in a higher-dimensional space is called the _______.
kernel trick
51
What mapping allows the kernel function to yield the same result as the dot product in a higher-dimensional space?
x j → φ ( x j )
52
What is the significance of the RBF kernel?
It allows for the calculation of K ( a, b ) even in infinite-dimensional spaces.
53
What does it mean for the RBF kernel to be a 'universal function approximator'?
It can find any decision boundary or function when mapped to lower-dimensional space.
54
Who introduced the polynomial kernel?
Tomaso Poggio
55
What did Vapnik's optimal margin classifier utilize to handle non-linear boundaries?
The kernel trick
56
What is the benefit of using an optimal margin classifier in high-dimensional space?
It helps find the best separating hyperplane, improving classification accuracy.
57
What was Guyon's contribution to the kernel trick and optimal margin classifiers?
She connected the ideas of optimal margin classifiers and the kernel trick, enabling more effective algorithms.
58
True or False: The kernel trick makes it easier to classify intermingled data classes.
True
59
What are artificial neural networks described as in terms of problem-solving?
Universal function approximators; given enough neurons, they can solve any problem.
60
What did the combination of Vapnik’s 1964 optimal margin classifier and the kernel trick achieve?
Allowed datasets that were previously off-limits to be analyzed, regardless of how intermingled the classes were.
61
What is the role of the kernel function in the optimal margin classifier?
It allows finding the best linearly separating hyperplane without computing in high-dimensional space.
62
What dataset did Boser primarily work on for testing the algorithm?
The Modified National Institute of Standards and Technology (MNIST) database of handwritten digits.
63
What was the significance of the Computational Learning Theory (COLT) conference for Guyon?
It was considered prestigious, and having a paper there indicated one was a serious machine learning person.
64
What was the title of the paper submitted by Guyon and Boser?
A Training Algorithm for Optimal Margin Classifiers.
65
What did Kristin Bennett’s Ph.D. work inspire Vapnik and Cortes to develop?
The soft-margin classifier.
66
What is the support vector network also known as?
Support vector machine (SVM).
67
What do support vector machines (SVMs) do?
They project datasets into high dimensions to find an optimal linearly separating hyperplane.
68
What are support vectors?
Data points that lie on the margins of no-one’s-land.
69
What did Vapnik's recognition contribute to the understanding of kernelized SVMs?
It highlighted their power and ensured the wider community understood it.
70
What is the Vapnik-Chervonenkis (VC) dimension?
A measure of an ML model’s capacity to classify data correctly.
71
What award did the BBVA Foundation give in 2020 related to SVMs?
Frontiers of Knowledge Award to Isabelle Guyon, Bernhard Schölkopf, and Vladimir Vapnik.
72
Fill in the blank: SVMs are now being used in _______.
[genomics, cancer research, neurology, diagnostic imaging, HIV drug cocktail optimization, climate research, geophysics, astrophysics]
73
What happened to the progress of neural networks after the introduction of SVMs?
The advancement of neural networks was derailed for a while.
74
Who inspired Guyon’s foray into machine learning?
John Hopfield.
75
What was the impact of Schölkopf and Smola's book on kernel methods?
It illustrated much of what one could do with the kernel trick.
76
True or False: Neural networks dominated machine learning in the nineties.
False.
77
What is the connection emerging between neural networks and kernel machines?
Theoretical advances are showing links between the two.

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