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Arg max
In mathematics, the arguments of the maxima (abbreviated arg max or argmax) are the points, or elements, of the domain of some function at which the function
Softmax function
considering the arg max as a function with categorical output 1 , … , n {\displaystyle 1,\dots ,n} (corresponding to the index), consider the arg max function
Orthogonal Procrustes problem
B ‖ F 2 − 2 ⟨ Ω A , B ⟩ = argmax Ω ⟨ Ω , B A T ⟩ = argmax Ω ⟨ Ω , U Σ V T ⟩ = argmax Ω ⟨ U T Ω V , Σ ⟩ = argmax Ω ⟨ S , Σ ⟩ where  S = U T
Viterbi algorithm
B_{iy_{j}})}} T 2 [ i , j ] ← argmax k ( T 1 [ k , j − 1 ] ⋅ A k i ⋅ B i y j ) {\displaystyle T_{2}[i,j]\gets \arg \max _{k}{(T_{1}[k,j-1]\cdot A_{ki}\cdot
Maximum likelihood estimation
{\displaystyle {\hat {\theta }}={\underset {\theta \in \Theta }{\operatorname {arg\;max} }}\ {\widehat {L}}_{n}(\theta \,;\mathbf {y} )} Intuitively, this selects
Restricted Boltzmann machine
treated as a visible vector v {\displaystyle v} ), argmax W ∏ v ∈ V P ( v ) {\displaystyle \arg \max _{W}\prod _{v\in V}P(v)} or equivalently, to maximize
Maximum a posteriori estimation
}(x)={\underset {\theta }{\operatorname {arg\,max} }}\ f(\theta \mid x)={\underset {\theta }{\operatorname {arg\,max} }}\ {\frac {f(x\mid \theta )\,g(\theta
Multiclass classification
confidence score: y ^ = arg max k ∈ { 1 … K } f k ( x ) {\displaystyle {\hat {y}}={\underset {k\in \{1\ldots K\}}{\arg \!\max }}\;f_{k}(x)} Although this
Principal component analysis
=1}{\operatorname {\arg \,max} }}\,\left\{\sum _{i}\left(t_{1}\right)_{(i)}^{2}\right\}={\underset {\Vert \mathbf {w} \Vert =1}{\operatorname {\arg \,max} }}\,\left\{\sum
Alternate reality game
An alternate reality game (ARG) is an interactive networked narrative that uses the real world as a platform and employs transmedia storytelling to deliver