[−][src]Enum opencv::ml::Boost_Types
Boosting type. Gentle AdaBoost and Real AdaBoost are often the preferable choices.
Variants
Discrete AdaBoost.
Real AdaBoost. It is a technique that utilizes confidence-rated predictions and works well with categorical data.
LogitBoost. It can produce good regression fits.
Gentle AdaBoost. It puts less weight on outlier data points and for that reason is often good with regression data.
Trait Implementations
impl Clone for Boost_Types
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fn clone(&self) -> Boost_Types
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fn clone_from(&mut self, source: &Self)
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impl Copy for Boost_Types
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impl Debug for Boost_Types
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impl PartialEq<Boost_Types> for Boost_Types
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fn eq(&self, other: &Boost_Types) -> bool
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#[must_use]fn ne(&self, other: &Rhs) -> bool
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impl StructuralPartialEq for Boost_Types
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Auto Trait Implementations
impl RefUnwindSafe for Boost_Types
impl Send for Boost_Types
impl Sync for Boost_Types
impl Unpin for Boost_Types
impl UnwindSafe for Boost_Types
Blanket Implementations
impl<T> Any for T where
T: 'static + ?Sized,
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T: 'static + ?Sized,
impl<T> Borrow<T> for T where
T: ?Sized,
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T: ?Sized,
impl<T> BorrowMut<T> for T where
T: ?Sized,
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T: ?Sized,
fn borrow_mut(&mut self) -> &mut T
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impl<T> From<T> for T
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impl<T, U> Into<U> for T where
U: From<T>,
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U: From<T>,
impl<T> ToOwned for T where
T: Clone,
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T: Clone,
type Owned = T
The resulting type after obtaining ownership.
fn to_owned(&self) -> T
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fn clone_into(&self, target: &mut T)
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impl<T, U> TryFrom<U> for T where
U: Into<T>,
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U: Into<T>,
type Error = Infallible
The type returned in the event of a conversion error.
fn try_from(value: U) -> Result<T, <T as TryFrom<U>>::Error>
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impl<T, U> TryInto<U> for T where
U: TryFrom<T>,
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U: TryFrom<T>,