Auto Topic: boundary
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| Auto Topic: boundary | CO_OCCURS | Propositional Logic | 8 |
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Evidence Chunks
| Source | Confidence | Mentions | Snippet |
|---|---|---|---|
textbook Artificial-Intelligence-A-Modern-Approach-4th-Edition.pdf | 0.63 | 5 | ... We also have an ordering on the hypothesis space, namely, generalization/specialization. This is a partial ordering, which means that each boundary will not be a point but rather a set of hypotheses called a boundary set. The great thing is that we can represent the entireBounda ... |
textbook Artificial-Intelligence-A-Modern-Approach-4th-Edition.pdf | 0.63 | 5 | owing boundary detection in (b) and region extraction in (c) and (d). One way to formalize the problem of detecting boundary curves is as a classification problem, amenable to the techniques of machine learning. A boundary curve at pixel location (x,y) will have an orientation θ. ... |
textbook Artificial-Intelligence-A-Modern-Approach-4th-Edition.pdf | 0.61 | 4 | ... ssification is to learn a hypothesis h that will take new (x1,x2) points and return either 0 for earthquakes or 1 for explosions. A decision boundary is a line (or a surface, in higher dimensions) that separates theDecision boundary two classes. In Figure 19.15(a), the decision bo ... |
textbook Artificial-Intelligence-A-Modern-Approach-4th-Edition.pdf | 0.59 | 3 | ... utput, being a number between 0 and 1, can be interpreted as a probability of belonging to the class labeled 1. The hypothesis forms a soft boundary in the input space and gives a probability of 0.5 for any input at the center of the boundary region, and approaches 0 or 1 as we m ... |
textbook Artificial-Intelligence-A-Modern-Approach-4th-Edition.pdf | 0.59 | 3 | ... in some cases we can ignore half of the examples. But not always. Sometimes the point we are querying for falls very close to the dividing boundary. The query point itself might be on the left hand side of the boundary, but one or more of the k nearest neighbors might actually b ... |
textbook Artificial-Intelligence-A-Modern-Approach-4th-Edition.pdf | 0.59 | 3 | ... ing set with positive examples as green filled cir- cles and negative examples as orange open circles. The true decision boundary, x2 1 + x2 2 ≤ 1, is also shown. (b) The same data after mapping into a three-dimensional input space (x2 1,x2 2, √ 2x1x2). The circular decision bound ... |
textbook Artificial-Intelligence-A-Modern-Approach-4th-Edition.pdf | 0.59 | 3 | ... olor, and texture. Within an object, or a single part of an object, these attributes vary relatively little, whereas across an inter-object boundary there is typically a large change in one or more of these attributes. We need to find a partition of the image into sets of pixels s ... |
textbook Artificial-Intelligence-A-Modern-Approach-4th-Edition.pdf | 0.57 | 2 | ofxq. (When the xi data points are equally spaced, these will be the two nearest neighbors.) In Figure 19.19, we show the decision boundary of k-nearest-neighbors classification for k = 1 and 5 on the earthquake data set from Figure 19.15. Nonparametric methods are still subject t ... |
textbook Artificial-Intelligence-A-Modern-Approach-4th-Edition.pdf | 0.57 | 2 | ... tains everything), and the S-set to contain False (the hypothesis whose extension is empty). Figure 20.4 shows the general structure of the boundary-set representation of the version space. To show that the representation is sufficient, we need the following two properties: 1. Eve ... |
textbook Artificial-Intelligence-A-Modern-Approach-4th-Edition.pdf | 0.57 | 2 | ... ter 19; the learning curve for decision tree learning is shown for comparison. A discriminative model directly learns the decision boundary between classes. That is, Discriminative model it learns P(Category|Inputs). Given example inputs, a discriminative model will come up with ... |
textbook Artificial-Intelligence-A-Modern-Approach-4th-Edition.pdf | 0.55 | 1 | ... t attributes are true. With real-valued attributes, the function y> A1 + A2 is hard to represent with a decision tree because the decision boundary is a diagonal line, and all decision tree tests divide the space up into rectangular, axis-aligned boxes. We would have to stack a l ... |
textbook Artificial-Intelligence-A-Modern-Approach-4th-Edition.pdf | 0.55 | 1 | ... cles) occurring between 1982 and 1990 in Asia and the Middle East (Kebeasy et al., 1998). Also shown is a decision boundary between the classes. (b) The same domain with more data points. The earthquakes and explosions are no longer linearly separable. invariant: Imagine a set of ... |
textbook Artificial-Intelligence-A-Modern-Approach-4th-Edition.pdf | 0.55 | 1 | ... r- thermore, the linear classifier always announces a completely confident prediction of 1 or 0, even for examples that are very close to the boundary; it would be better if it could classify some examples as a clear 0 or 1, and others as unclear borderline cases. All of these issu ... |
textbook Artificial-Intelligence-A-Modern-Approach-4th-Edition.pdf | 0.55 | 1 | ... and left ofxq. (When the xi data points are equally spaced, these will be the two nearest neighbors.) In Figure 19.19, we show the decision boundary of k-nearest-neighbors classification for k = 1 and 5 on the |
textbook Artificial-Intelligence-A-Modern-Approach-4th-Edition.pdf | 0.55 | 1 | ... 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 linear separating hyperplan ... |
textbook Artificial-Intelligence-A-Modern-Approach-4th-Edition.pdf | 0.55 | 1 | ... e 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 proportional to the distance required to move them back to the correct side. The kernel method can be a ... |
textbook Artificial-Intelligence-A-Modern-Approach-4th-Edition.pdf | 0.55 | 1 | ... to reduce bias. The hypothesis space of a base model may be too restrictive, imposing a strong bias (such as the bias of a linear decision boundary in logistic regression). An ensemble can be more expressive, and thus have less bias, than the base models. Figure 19.23 shows that ... |
textbook Artificial-Intelligence-A-Modern-Approach-4th-Edition.pdf | 0.55 | 1 | ... We also have an ordering on the hypothesis space, namely, generalization/specialization. This is a partial ordering, which means that each boundary will not be a point |