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Machine learning has become a powerful tool in real applications such as decision making, sentiment prediction and ontology engineering. In the form of learning strategies, machine learning can be specialized into two types: supervised learning and unsupervised learning. Classification is a special type of supervised learning task, which can also be referred to as categorical prediction. In other...
Based on the characteristics of Indonesian and Japanese language, we did several experiments on the additional process to an Indonesian-Japanese statistical machine translation (SMT). We proposed several additional processes such as employing the POS tag information, adding the size of monolingual target corpus, using Indonesian stemmer in Indonesian to Japanese translation, eliminating Japanese particle...
A new transfer learning method is presented in this paper, addressing a particularly hard transfer learning problem: the case where the target domain shares only a subset of its classes with the source domain and only unlabeled data are provided for the target domain. This is a situation that occurs frequently in real-world applications, such as the multiclass document classification problems that...
In this paper we present a system that is capable of tracking the pitch and volume of a musical source by making use of training data. We show how we can use pitch-tagged training example sounds to construct a model of a target source, and then use that model to track such a source in unseen mixtures. We do so using a regularized decomposition approach that is designed to strive for semantic continuity...
This paper presents a new probabilistic neural network model, called IPNN (for Incremental Probabilistic Neural Network), which is able to learn continuously probability distributions from data flows. The proposed model is inspired in the Specht's general regression neural network, but have several improvements which makes it more suitable to be used in on-line and robotic tasks. Moreover, IPNN is...
We propose a nonparametric, probabilistic model for the automatic segmentation of medical images, given a training set of images and corresponding label maps. The resulting inference algorithms rely on pairwise registrations between the test image and individual training images. The training labels are then transferred to the test image and fused to compute the final segmentation of the test subject...
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