| 31. | A value equal to 1.0 indicates all data fit perfectly within the hyperplane.
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| 32. | Together with the weights, the threshold defines a dividing hyperplane in the instance space.
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| 33. | In that case, the intersection point mentioned above lies on the hyperplane at infinity.
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| 34. | The locus " t " = 0 is called the hyperplane at infinity.
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| 35. | The supporting hyperplane theorem is a special case of the Hahn Banach theorem of functional analysis.
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| 36. | These two convex, non-intersecting sets allow us to apply the separating hyperplane theorem.
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| 37. | The exponents of the monomials of a critical Lagrangian define a hyperplane in an exponent space.
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| 38. | A hyperplane is a subspace of one dimension less than the dimension of the full space.
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| 39. | Where is a given smooth projective variety in the ambient projective space and is a hyperplane.
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| 40. | Other similar methods, such as Maximum Marginal Hyperplane, choose data with the largest W.
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