ABSTRACT Machine (TSVM) model and compares it with the traditional inductive SVM on a key problem in Bioinfonnatics -promoter recognition. While inductive reasoning is concerned with the development of a model (a function) to approximate data from the whole pmhlem space (induction), and consecutively using this model to predict output values for a new input vector (deduction), in the transductive inference systems a model is developed for every new input vector based on some closest to the new vector data fmm an existing database and this model is used to predict only the output for this vector. The TSVM outperforms by far the inductive SVM models applied on the same problems. Analysis IS given on the advantages and disadvantages of the TSVM. Hybrid TSVM-evolving connections systems are discussed as directions for future research. Most of the leaming models and systems in artificial intelligence apply inductive inference where a model (a function) is derived from data and this model is fwther applied on new data. [I]. This is the case in the area of soft
REF More specifically, the authors introduce a novel Transductive Support Vector Machine (TSVM), which develops a model for every new input vector, based on specific training examples and then uses the model to predict the output only for the specific input vector.
Transductive support vector machines and applications in bioinformatics for promoter recognition
International Conference on Neural Networks and Signal Processing, 2003. Proceedings of the 2003
International Conference on Neural Networks and Signal Processing, 2003. Proceedings of the 2003
Computer Science