Auto Topic: bin
auto_bin | topic
Coverage Score
1
Mentioned Chunks
5
Mentioned Docs
1
Required Dimensions
definitionpros_cons
Covered Dimensions
definitionpros_cons
Keywords
bin
Relations
| Source | Type | Target | W |
|---|---|---|---|
| Auto Topic: bin | CO_OCCURS | Auto Topic: hash | 4 |
| Auto Topic: bin | CO_OCCURS | Auto Topic: near | 4 |
Evidence Chunks
| Source | Confidence | Mentions | Snippet |
|---|---|---|---|
textbook Artificial-Intelligence-A-Modern-Approach-4th-Edition.pdf | 0.59 | 3 | ... (a line). In fact, we can discretize the line into bins—hash buckets—so that, with high prob- ability, near points project down to the same bin. Points that are far away from each other will tend to project down into different bins, but there will always be a few projections that ... |
textbook Artificial-Intelligence-A-Modern-Approach-4th-Edition.pdf | 0.59 | 3 | ions that coincidentally project far-apart points into the same bin. Thus, the bin for point xq contains many (but not all) points that are near xq, and it might contain some points that are far away. 708 Chapter 19 Learning from Examples 0 1 2 3 4 5 6 7 8 0 2 4 6 8 10 12 14 0 1 ... |
textbook Artificial-Intelligence-A-Modern-Approach-4th-Edition.pdf | 0.55 | 1 | ... s rely on anexact match? Hash codes randomly distribute values among the bins, but we want to have near points grouped together in the same bin; we want a locality-sensitive hash (LSH). Locality-sensitive hash We can’t use hashes to solve NN(k,xq) exactly, but with a clever use o ... |
textbook Artificial-Intelligence-A-Modern-Approach-4th-Edition.pdf | 0.55 | 1 | et of points in bin gi(xq) of each hash table, and union these ℓ sets together into a set of candidate points, C. Then we compute the actual distance to xq for each of the points in C and return the k closest points. With high probability, each of the points that are near to xq w ... |
textbook Artificial-Intelligence-A-Modern-Approach-4th-Edition.pdf | 0.55 | 1 | ... l for a range of scales. This range usually runs from a few pixels to the extent of the image. Now divide the patch into bins, and in each bin construct an orientation histogram, then summarize the pattern of histograms across bins. It is no longer usual to construct these descri ... |