cnam.marc/cnam/travaux/probatoire/présentation/principles.tex

\section{Principes}
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\begin{frame}{\bititle\\Principes}
\begin{columns}\begin{column}{.5\textwidth}

\begin{itemize}
\item<1-> filtrage email
\item<2-> classification image
\item<3-> détection stéganographie
\item<4-> granularité texture
\item<5-> reconnaissance texte
\item<6-> expression faciale
\item<7-> reconnaissance vocale
\item<8-> structure protéine
\item<9-> diagnostic médical
\item<10-> catégorisation texte
\end{itemize}

\end{column}\begin{column}{.5\textwidth}


\end{column}\end{columns}
\end{frame}
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\subsection{Régression}
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\begin{frame}{\bititle\\Principes / Régression / Linéaire}
\begin{center}

\only<1>{Régression linéaire, variation d’$\varepsilon$\\
 \\
\imgbig{regression_linear_left}}

\only<2>{Régression linéaire, variation d’$\varepsilon$\\
 \\
\imgbig{regression_linear_right}}

\end{center}
\end{frame}
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\begin{frame}{\bititle\\Principes / Régression / Non linéaire}
\begin{center}

\only<1>{Régression polynomiale de degré 2, variation de $C$\\
 \\
\imgbig{regression_nonlinear_left}}

\only<2>{Régression polynomiale de degré 2, variation de $C$\\
 \\
\imgbig{regression_nonlinear_right}}

\end{center}
\end{frame}
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\subsection{Classification}
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\begin{frame}{\bititle\\Principes / Classification / Linéaire / Vaste}
\begin{center}

\only<1>{Séparation à Vaste Marge\\
 \\
\imgwide{margin_large_left}}

\only<2>{Séparation à Vaste Marge\\
 \\
\imgwide{margin_large_right}}

\end{center}
\end{frame}
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\begin{frame}{\bititle\\Principes / Classification / Linéaire / Vaste}
\begin{center}

\only<1>{\imgwide{margin_scale_left}}

\only<2>{\imgwide{margin_scale_right}}

\end{center}
\end{frame}
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\begin{frame}{\bititle\\Principes / Classification / Linéaire / Souple}
\begin{center}

\only<1>{Sensibilité de vaste marge aux anomalies\\
 \\
\imgwide{margin_hard_left}}

\only<2>{Sensibilité de vaste marge aux anomalies\\
 \\
\imgwide{margin_hard_right}}

\end{center}
\end{frame}
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\begin{frame}{\bititle\\Principes / Classification / Linéaire / Souple}
\begin{center}

\only<1>{Plus ou moins de violations de marge, variation de $C$\\
 \\
\imgwide{margin_few_left}}

\only<2>{Plus ou moins de violations de marge, variation de $C$\\
 \\
\imgwide{margin_few_right}}

\end{center}
\end{frame}
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\begin{frame}{\bititle\\Principes / Classification / Non linéaire}
\begin{center}

\only<1>{Séparation linéaire par ajout de variable\\
 \\
\imgwide{nonlinear_linear_left}}

\only<2>{Séparation linéaire par ajout de variable\\
 \\
\imgwide{nonlinear_linear_right}}

\end{center}
\end{frame}
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\begin{frame}{\bititle\\Principes / Classification / Non linéaire}
\begin{center}

\only<1>{Classification utilisant des variables polynomiales\\
 \\
\imgbig{features_polynomial}}

\end{center}
\end{frame}
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\begin{frame}{\bititle\\Principes / Classification / Non linéaire}
\begin{center}

\only<1>{Fonction noyau polynomiale\\
 \\
\imgbig{kernel_polynomial_left}}

\only<2>{Fonction noyau polynomiale\\
 \\
\imgbig{kernel_polynomial_right}}

\end{center}
\end{frame}
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\begin{frame}{\bititle\\Principes / Classification / Non linéaire}
\begin{center}

\only<1>{Variables de similarité utilisant la RBF gaussienne\\
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\imgbig{features_similar_left}}

\only<2>{Variables de similarité utilisant la RBF gaussienne\\
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\imgbig{features_similar_right}}

\end{center}
\end{frame}
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\begin{frame}{\bititle\\Principes / Classification / Non linéaire}
\begin{center}

\only<1>{Fonction noyau gaussienne (RBF)\\
 \\
\imgbig{kernel_rbf_left}
\imgbig{kernel_rbf_right}}

\end{center}
\end{frame}
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\begin{frame}{\bititle\\Principes / Classification / Multi-classes}

\end{frame}
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\begin{frame}{\bititle\\Principes / Classification / Optimisation}

\end{frame}