Dontopedia

SVC

From Dontopedia, the open, paraconsistent wiki. (Last updated 2026-06-09.)

SVC has 9 facts recorded in Dontopedia across 1 reference, with 3 live disagreements.

9 facts·4 predicates·1 sources·3 in dispute

Mostly:has parameter c(3), has parameter kernel(2), has parameter gamma(2)

Maturity scale raw canonical shape-checked rule-derived certified

Inbound mentions (1)

Other subjects in dontopedia point AT this entity as a value. These are inverse relationships — e.g. "X motherOf this subject" — and answer questions the forward facts can't. Grouped by predicate.

containsContains(1)

Other facts (8)

The long tail: predicates that appear too rarely to warrant their own section. Filter or scroll to find a specific one. Each row links to its source.

8 facts
PredicateValueRef
Has Parameter C0.1[1]
Has Parameter C1[1]
Has Parameter C10[1]
Has Parameter Kernellinear[1]
Has Parameter Kernelrbf[1]
Has Parameter Gammascale[1]
Has Parameter Gammaauto[1]
Rdf:typeSvc[1]

Timeline

Timeline axis is valid_time — when each source says the fact was true in the world, not when Dontopedia learned about it. Retracted rows are kept for provenance; coloured stripes indicate the context kind.

typebeam/0daa7c15-b2c7-44ef-a5e9-390bf6864c0a
ex:SVC
labelbeam/0daa7c15-b2c7-44ef-a5e9-390bf6864c0a
SVC
hasParameterCbeam/0daa7c15-b2c7-44ef-a5e9-390bf6864c0a
0.1
hasParameterCbeam/0daa7c15-b2c7-44ef-a5e9-390bf6864c0a
1
hasParameterCbeam/0daa7c15-b2c7-44ef-a5e9-390bf6864c0a
10
hasParameterKernelbeam/0daa7c15-b2c7-44ef-a5e9-390bf6864c0a
linear
hasParameterKernelbeam/0daa7c15-b2c7-44ef-a5e9-390bf6864c0a
rbf
hasParameterGammabeam/0daa7c15-b2c7-44ef-a5e9-390bf6864c0a
scale
hasParameterGammabeam/0daa7c15-b2c7-44ef-a5e9-390bf6864c0a
auto

References (1)

1 references
  1. ctx:claims/beam/0daa7c15-b2c7-44ef-a5e9-390bf6864c0a
    • full textbeam-chunk
      text/plain1 KBdoc:beam/0daa7c15-b2c7-44ef-a5e9-390bf6864c0a
      Show excerpt
      df = pd.read_csv('data.csv') # Split the data into training and testing sets X_train, X_test, y_train, y_test = train_test_split(df['text'], df['label'], test_size=0.2, random_state=_42) # Feature extraction vectorizer = TfidfVectorizer()

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