Learning Path
Question & Answer
Choose the Best Answer
Embed classes as vectors → Calculate signed distances → Determine closest vector → Classify the data point
Classify the data point → Embed classes as vectors → Determine closest vector → Calculate signed distances
Calculate signed distances → Determine closest vector → Embed classes as vectors → Classify the data point
Determine closest vector → Classify the data point → Calculate signed distances → Embed classes as vectors
Understanding the Answer
Let's break down why this is correct
First, each class is turned into a vector. Other options are incorrect because This answer says to classify first, but you cannot know the class before doing any calculations; It starts with distances before the vectors exist, so the distances cannot be computed.
Key Concepts
Nearest-Neighbor Un-embedding
medium level question
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Deep Dive: Nearest-Neighbor Un-embedding
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Definition
Nearest-neighbor un-embedding involves embedding classes as vectors and determining the closest vector to a given prediction. It focuses on calculating signed distances to decision boundaries for effective classification.
Topic Definition
Nearest-neighbor un-embedding involves embedding classes as vectors and determining the closest vector to a given prediction. It focuses on calculating signed distances to decision boundaries for effective classification.
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