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Constitutive modeling of anisotropic plastic material behavior traditionally follows a deductive scheme, relying on empirical observations that are cast into analytic equations, the so-called phenomenological yield functions. Recently, data-driven constitutive modeling has emerged as an alternative to phenomenological models as it offers a more general way to describe the material behavior with no or fewer assumptions. In data-driven constitutive modeling, methods of statistical learning are applied to infer the yield function directly from a data set generated by experiments or numerical simulations. Currently these data sets solely consist of stresses and strains, considering the microstructure only implicitly. Similar to the phenomenological approach, this limits the generality of the inferred material model, as it is only valid for the specific material employed in the virtual or physical experiments. In this work, we present an approach to overcome this limitation by incorporating explicitly microstructural information into the yield function. The approach builds upon the support vector machine (SVM) for ideal plasticity [2] and extends its feature space from stress only to stress+ texture. The texture is described by the coefficients of the general spherical harmonics (GSH) series expansion of the orientation distribution function. We create a data set that contains the yield points in 300 different directions for over 7000 textures by using full-field crystal plasticity simulations. A single SVM classifier is trained on this data set to find the class boundary between elastic and plastic data points dependent on stress and texture.