![]() ![]() In: 2011 International Conference on Virtual Reality and Visualization (ICVRV), pp. Sun, Z., Lu, S., Guo, X., Tian, Y.: Leaf vein and contour extraction from point cloud data. In: AFITA International Conference, the Quality Information for Competitive Agricultural based Production System and Commerce, pp. Rahmadhani, M., Herdiyeni, Y.: Shape and vein extraction on plant leaf images using fourier and B-spline modeling. In: 2013 International Conference on Advances in Technology and Engineering (ICATE), pp. Rankothge, W.H., et al.: Plant recognition system based on Neural Networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 71, 1–13 (2017)Ĭhai, D., Forstner, W., Lafarge, F.: Recovering line-networks in images by junction-point processes. Lee, S.H., Chan, C.S., Mayo, S.J., Remagnino, P.: How deep learning extracts and learns leaf features for plant classification. In: NAECON 2018-IEEE National Aerospace and Electronics Conference, pp. Īli, R., Hardie, R., Essa, A.: A leaf recognition approach to plant classification using machine learning. In: Bellot, P., Trabelsi, C., Mothe, J., Murtagh, F., Nie, J.Y., Soulier, L., SanJuan, E., Cappellato, L., Ferro, N. Lee, S.H., Chang, Y.L., Chan, C.S., Alexis, J., Bonnet, P., Goeau, H.: Plant classification based on gated recurrent unit. In: 2015 IEEE International Conference on Image Processing (ICIP), pp. Lee, S.H., Chan, C.S., Wilkin, P., Remagnino, P.: Deep-plant: plant identification with convolutional neural networks. Advances in Intelligent Systems and Computing AISC. (eds.) Proceedings of the Second International Conference on Computer and Communication Technologies. In: Satapathy, S., Raju, K., Mandal, J., Bhateja, V. Salve, P., Sardesai, M., Manza, R., Yannawar, P.: Identification of the plants based on leaf shape descriptors. In: Thampi, S.M., Marques, O., Krishnan, S., Li, K.-C., Ciuonzo, D., Kolekar, M.H. Salve, P., Sardesai, M., Yannawar, P.: Classification of plants using GIST and LBP score level fusion. But you have to learn some basic knowledge about Hadoop.Īnd for machine learning, weka is a software toolkit for experiences which integrates many algorithms.Salve, P., Yannawar, P., Sardesai, M.: Multimodal plant recognition through hybrid feature fusion technique using imaging and non-imaging hyper-spectral data. It integrates three aspects' algorithms: recommendation, clustering, and classification. These are all from my experiences, but for your case, you have no better ways to decide which methods to use but to try every algorithm to fit your model.Īpache's Mahout is a great tool for machine learning algorithms. Because I think decision tree need several key nodes, while it's hard to find "several key tokens" for text classification, and random forest works bad for high sparse dimensions. I've never try this method for text classification. But the dictionary of the dataset always has dirty tokens. It sometime works good, but from my experiences, it has bad performance in text classification, as it has high demands for good tokenizers (filters). SVM has SVC(classification) and SVR(Regression) algorithms to do class classification and prediction. I think you misunderstand the conception of clustering and classification. KNN is for clustering rather than classification. And I would try this algorithm first for sure. Though this is the simplest algorithm and everything is deemed independent, in real text classification case, this method work great. ![]()
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