Auto Topic: margin
auto_margin | topic
Coverage Score
1
Mentioned Chunks
22
Mentioned Docs
2
Required Dimensions
definitionpros_cons
Covered Dimensions
definitionpros_cons
Keywords
margin
Relations
| Source | Type | Target | W |
|---|---|---|---|
| Auto Topic: margin | CO_OCCURS | Auto Topic: separator | 6 |
| Auto Topic: kernel | CO_OCCURS | Auto Topic: margin | 6 |
| Auto Topic: margin | CO_OCCURS | Auto Topic: self | 5 |
| Auto Topic: margin | CO_OCCURS | Propositional Logic | 5 |
| Auto Topic: margin | CO_OCCURS | Auto Topic: perceptron | 4 |
| Auto Topic: def | CO_OCCURS | Auto Topic: margin | 4 |
| Auto Topic: margin | CO_OCCURS | Auto Topic: messagebox | 3 |
| Auto Topic: margin | CO_OCCURS | Auto Topic: square_size | 3 |
| Auto Topic: config | CO_OCCURS | Auto Topic: margin | 3 |
| Auto Topic: fill | CO_OCCURS | Auto Topic: margin | 3 |
Evidence Chunks
| Source | Confidence | Mentions | Snippet |
|---|---|---|---|
assignments CIS5210-Assignments/M2/homework2_gui.py | 0.65 | 6 | ... egal value", "Length is less than N.\nPlease try again", parent=self) return 0 self.result = (length, n, self.__check.get()) return 1 MARGIN = 10 class LinearDisks(tk.Frame): def __init__(self, parent, length, n, distinct): super().__init__(parent) self.__length = length self.__s ... |
textbook Artificial-Intelligence-A-Modern-Approach-4th-Edition.pdf | 0.63 | 5 | ... tion: (a) Two classes of points (orange open and green filled circles) and three candidate linear separators. (b) The maximum margin separator (heavy line), is at the midpoint of the margin (area between dashed lines). The support vectors (points with large black circles) are the ... |
textbook Artificial-Intelligence-A-Modern-Approach-4th-Edition.pdf | 0.61 | 4 | e call this separator, shown in Figure 19.21(b) the maximum margin separator. The margin Maximum margin separator Marginis the width of the area bounded by dashed lines in the figure—twice the distance from the separator to the nearest example point. Now, how do we find this separa ... |
assignments CIS5210-Assignments/M2/homework2_gui.py | 0.61 | 4 | ... nvas.create_line( (fr + .5) * SQUARE_SIZE, SQUARE_SIZE / 2, (to + .5) * SQUARE_SIZE, SQUARE_SIZE / 2, arrow=tk.LAST, arrowshape=(MARGIN, MARGIN * 2, MARGIN), fill='orange', width=MARGIN / 2) self.__cur_sol += 1 if self.__cur_sol >= len(self.__solutions): self.__next_btn.config(st ... |
textbook Artificial-Intelligence-A-Modern-Approach-4th-Edition.pdf | 0.59 | 3 | ... e bibliographical notes), or for self-study or as a reference. Throughout the book, important points are marked with a triangle icon in the margin.▶ Wherever a new term is defined, it is also noted in the margin. Subsequent significant usesTerm of the term are in bold, but not in t ... |
assignments CIS5210-Assignments/M2/homework2_gui.py | 0.59 | 3 | * SQUARE_SIZE - MARGIN, SQUARE_SIZE - MARGIN, fill='black'), self.__canvas.create_text( (i + .5) * SQUARE_SIZE, SQUARE_SIZE / 2, text=i, font=(None, MARGIN * 3), fill='white' if distinct else '')) self.__next_btn = tk.Button(self.__buttons, text="Next move", command=self.__next, ... |
textbook Artificial-Intelligence-A-Modern-Approach-4th-Edition.pdf | 0.57 | 2 | ... iginal hypothesis space. does not cleanly separate the classes, but reflects the reality of the noisy data. That is pos- sible with the soft margin classifier, which allows examples to fall on the wrong side of theSoft margin decision boundary, but assigns them a penalty proportion ... |
textbook Artificial-Intelligence-A-Modern-Approach-4th-Edition.pdf | 0.55 | 1 | quent significant usesTerm of the term are in bold, but not in the margin. We have included a comprehensive index and an extensive bibliography. The only prerequisite is familiarity with basic concepts of computer science (algorithms, data structures, complexity) at a sophomore le ... |
textbook Artificial-Intelligence-A-Modern-Approach-4th-Edition.pdf | 0.55 | 1 | ... 10 0 Figure 11.15 A solution to the job-shop scheduling problem from Figure 11.13, taking into account resource constraints. The left-hand margin lists the three reusable resources, and actions are shown aligned horizontally with the resources they use. There are two possi- ble s ... |
textbook Artificial-Intelligence-A-Modern-Approach-4th-Edition.pdf | 0.55 | 1 | ... now been taken over by deep learning networks and random forests, but SVMs retain three attractive properties: 1. SVMs construct a maximum margin separator—a decision boundary with the largest possible distance to example points. This helps them generalize well. 2. SVMs create a ... |
textbook Artificial-Intelligence-A-Modern-Approach-4th-Edition.pdf | 0.55 | 1 | ... ginal hypothesis space. does not cleanly separate the classes, but reflects the reality of the noisy data. That is pos- sible with the soft margin |
textbook Artificial-Intelligence-A-Modern-Approach-4th-Edition.pdf | 0.55 | 1 | ... arameters. Examples include nearest neighbors and locally weighted regression. • Support vector machines find linear separators with maximum margin to improve the generalization performance of the classifier. Kernel methods implicitly transform the input data into a high-dimensiona ... |
textbook Artificial-Intelligence-A-Modern-Approach-4th-Edition.pdf | 0.55 | 1 | t vector machines find linear separators with maximum margin to improve the generalization performance of the classifier. Kernel methods implicitly transform the input data into a high-dimensional space where a linear separator may exist, even if the original data are nonseparable. ... |
textbook Artificial-Intelligence-A-Modern-Approach-4th-Edition.pdf | 0.55 | 1 | ... and his 736 Chapter 19 Learning from Examples colleagues (Boser et al., 1992). SVMs were made practical with the introduction of the soft- margin classifier for handling noisy data in a paper that won the 2008 ACM Theory and Prac- tice Award (Cortes and Vapnik, 1995), and of the S ... |
textbook Artificial-Intelligence-A-Modern-Approach-4th-Edition.pdf | 0.55 | 1 | ... L. (Ed.), Encyclopedia of Cognitive Science. Macmil- lan. Boser, B., Guyon, I., and Vapnik, V . N. (1992). A training algorithm for optimal margin classifiers. In COLT-92. Bosse, M., Newman, P., Leonard, J., Soika, M., Feiten, W., and Teller, S. (2004). Simultaneous localization a ... |
textbook Artificial-Intelligence-A-Modern-Approach-4th-Edition.pdf | 0.55 | 1 | ... reund, Y . and Schapire, R. E. (1996). Experiments with a new boosting algorithm. In ICML-96. Freund, Y . and Schapire, R. E. (1999). Large margin classification using the perceptron algorithm.Machine Learning, 37, 277–296. Frey, B. J. (1998). Graphical models for machine learning ... |
textbook Artificial-Intelligence-A-Modern-Approach-4th-Edition.pdf | 0.55 | 1 | 9). Large margin classification using the perceptron algorithm.Machine Learning, 37, 277–296. Frey, B. J. (1998). Graphical models for machine learning and digital communication. MIT Press. Frey, C. B. and Osborne, M. A. (2017). The future of employment: How susceptible are jobs t ... |
textbook Artificial-Intelligence-A-Modern-Approach-4th-Edition.pdf | 0.55 | 1 | ... for airport time slot al- location. Bell Journal of Economics, 13, 402–417. Ratliff, N., Bagnell, J. A., and Zinkevich, M. (2006). Maximum margin planning. In ICML-06. Ratliff, N., Zucker, M., Bagnell, J. A., and Srinivasa, S. (2009). CHOMP: Gradient optimization techniques for ... |