Support vector machine (SVM), developed by Vapnik et al., is a new and promising technique for classification and regression and has been proved to be competitive with the best available learning machines in many applications. However, the classification speed of SVM is substantially slower than that of other techniques with similar generalization ability. A new type SVM named projected SVM (PSVM), which is a combination of feature vector selection (FVS) method and linear SVM (LSVM), is proposed in present paper. In PSVM, the FVS method is first used to select a relevant subset (feature vectors, FVs) from the training data, and then both the training data and the test data are projected into the subspace constructed by FVs, and finally linear SVM (LSVM) is applied to classify the projected data. The time required by PSVM to calculate the class of new samples is proportional to the count of FVs. In most cases, the count of FVs is smaller than that of support vectors (SVs), and therefore PSVM is faster th Support vector machine (SVM), developed by Vapnik et al., Is a new and promising technique for classification and regression and has been proved to be competitive with the best available learning machines in many applications. However, slower than that of other techniques with similar generalization ability. A new type SVM named projected SVM (PSVM), which is a combination of feature selection (FVS) method and linear SVM (LSVM), is proposed in present paper. the FVS method is first used to select a relevant subset (FVs) from the training data, and then both the training data and the test data are projected into the subspace constructed by FVs, and finally linear SVM (LSVM) is applied To classify the projected data. The time required by PSVM to calculate the class of new samples is proportional to the count of FVs. In most cases, the count of FVs is smaller than that of support vectors (SVs), and therefore PSVM is faster th