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In machine learning, feature hashing, also … In machine learning, feature hashing, also known as the hashing trick (by analogy to the kernel trick), is a fast and space-efficient way of vectorizing features, i.e. turning arbitrary features into indices in a vector or matrix. It works by applying a hash function to the features and using their hash values as indices directly, rather than looking the indices up in an associative array. This trick is often attributed to Weinberger et al. (2009), but there exists a much earlier description of this method published by John Moody in 1989.is method published by John Moody in 1989.
, 機械学習において、Feature Hashing（フィーチャーハッシング）は、高速かつ省メモリな特徴量をベクトルに変換する手法であり、任意の特徴をベクトルあるいは行列のインデックスに変換する。kernel trick(カーネルトリック)に似せてHashing Trick（ハッシュトリック）とも呼ばれる。連想配列を走査するのではなく、ハッシュ関数を特徴量に適用し、その値をインデックスとして直接使用する。
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https://ml.dask.org/modules/generated/dask_ml.feature_extraction.text.HashingVectorizer.html%23dask_ml.feature_extraction.text.HashingVectorizer +
, https://web.archive.org/web/20120609232923/http:/metaoptimize.com/qa/questions/6943/what-is-the-hashing-trick +
, http://hunch.net/~jl/projects/hash_reps/index.html +
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36126852
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19896
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1114513799
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http://dbpedia.org/resource/Robert_Burton +
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If the binary hash is unbiased , then is an isometry in expectation:
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| http://dbpedia.org/property/name
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Theorem
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| http://dbpedia.org/property/proof
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By linearity of expectation, Now, , since we assumed is unbiased. So we continue
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Part 2, Sect. II, Mem. IV.
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By this art you may contemplate the variation of the 23 letters...
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http://dbpedia.org/resource/The_Anatomy_of_Melancholy +
, Proof
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| rdfs:comment |
機械学習において、Feature Hashing（フィーチャーハッシング）は、高速かつ省メモリな特徴量をベクトルに変換する手法であり、任意の特徴をベクトルあるいは行列のインデックスに変換する。kernel trick(カーネルトリック)に似せてHashing Trick（ハッシュトリック）とも呼ばれる。連想配列を走査するのではなく、ハッシュ関数を特徴量に適用し、その値をインデックスとして直接使用する。
, In machine learning, feature hashing, also … In machine learning, feature hashing, also known as the hashing trick (by analogy to the kernel trick), is a fast and space-efficient way of vectorizing features, i.e. turning arbitrary features into indices in a vector or matrix. It works by applying a hash function to the features and using their hash values as indices directly, rather than looking the indices up in an associative array. This trick is often attributed to Weinberger et al. (2009), but there exists a much earlier description of this method published by John Moody in 1989.is method published by John Moody in 1989.
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| rdfs:label |
Feature hashing
, Feature Hashing
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