. "[24F2A8535D6D]" . . "RIV/67985556:_____/11:00368741!RIV12-AV0-67985556" . "The purpose of feature selection in machine learning is at least two-fold \u2013 saving measurement acquisition costs and reducing the negative effects of the curse of dimensionality with the aim to improve the accuracy of the models and the classification rate of classifiers with respect to previously unknown data. Yet it has been shown recently that the process of feature selection itself can be negatively affected by the very same curse of dimensionality \u2013 feature selection methods may easily over-fit or perform unstably. Such an outcome is unlikely to generalize well and the resulting recognition system may fail to deliver the expectable performance. In many tasks, it is therefore crucial to employ additional mechanisms of making the feature selection process more stable and resistant the curse of dimensionality effects. In this paper we discuss three different approaches to reducing this problem." . "P(1M0572), P(2C06019), P(GA102/08/0593), Z(AV0Z10750506)" . "Improving feature selection resistance to failures caused by curse-of-dimensionality"@en . . . . . "4"^^ . "The purpose of feature selection in machine learning is at least two-fold \u2013 saving measurement acquisition costs and reducing the negative effects of the curse of dimensionality with the aim to improve the accuracy of the models and the classification rate of classifiers with respect to previously unknown data. Yet it has been shown recently that the process of feature selection itself can be negatively affected by the very same curse of dimensionality \u2013 feature selection methods may easily over-fit or perform unstably. Such an outcome is unlikely to generalize well and the resulting recognition system may fail to deliver the expectable performance. In many tasks, it is therefore crucial to employ additional mechanisms of making the feature selection process more stable and resistant the curse of dimensionality effects. In this paper we discuss three different approaches to reducing this problem."@en . "3"^^ . . "Grim, Ji\u0159\u00ED" . . . "Improving feature selection resistance to failures caused by curse-of-dimensionality"@en . "000293207900007" . "203968" . . . "Kybernetika" . "Improving feature selection resistance to failures caused by curse-of-dimensionality" . . . "47" . "CZ - \u010Cesk\u00E1 republika" . "3" . . . . . . "RIV/67985556:_____/11:00368741" . . . "0023-5954" . . "Novovi\u010Dov\u00E1, Jana" . . "25"^^ . . "Somol, Petr" . "Improving feature selection resistance to failures caused by curse-of-dimensionality" . "Pudil, P." . . "feature selection; curse of dimensionality; over-fitting; stability; machine learning; dimensionality reduction"@en . .