The taxonomic classification of asteroids has been mostly based on spectroscopic observations with wavelengths spanning from the visible (VIS) to the near-infrared (NIR). VIS-NIR spectra of ~2500 asteroids have been obtained since the 1970s; the Sloan Digital Sky Survey (SDSS) Moving Object Catalog 4 (MOC 4) was released with ~4x105 measurements of asteroid positions and colors in the early 2000s. A number of works then devised methods to classify these data within the framework of existing taxonomic systems. Some of these works, however, used 2D parameter space (e.g., gri slope vs. z-i color) that displayed a continuous distribution of clouds of data points resulting in boundaries that were artificially defined. We introduce here a more advanced method to classify asteroids based on existing systems. This approach is simply represented by a triplet of SDSS colors. The distributions and memberships of each taxonomic type are determined by machine learning methods in the form of both unsupervised and semi-supervised learning. We apply our scheme to MOC 4 calibrated with VIS-NIR reflectance spectra. We successfully separate seven different taxonomy classification (C, D, K, L, S, V, and X) with which we have a sufficient number of spectroscopic datasets. We found the overlapping regions of taxonomic types in a 2D plane were separated with relatively clear boundaries in the 3D space newly defined in this work. Our scheme explicitly discriminates between different taxonomic types (e.g., K and X types), which is an improvement over existing systems. This new method for taxonomic classification has a great deal of scalability for asteroid research, such as space weathering in the S-complex, and the origin and evolution of asteroid families. We present the structure of the asteroid belt, and describe the orbital distribution based on our newly assigned taxonomic classifications. It is also possible to extend the methods presented here to other photometric systems, such as the Johnson-Cousins and LSST filter systems.