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In many machine learning settings, labeled samples are difficult to collect while unlabeled samples are abundant. We investigate in this paper the design of support vector machine classification algorithms learning from positive and unlabeled samples only. We first find the minimum bounding sphere that enclosed all the positive samples, and then use this minimum bounding sphere to pick out the negative...
Emotion recognition is an important module in affective computing. It is usually studied based on facial and audio information with methodologies such as ANN, fuzzy set, SVM, HMM, etc. In this paper, a novel approach based on selective ensemble is proposed for emotion recognition. Simulation experiments prove that the proposed method has better performance than the method of single classifier, even...
The following topics are dealt with: AI and expert systems; artificial immune systems and bio-informatics; chaos theory; data mining; fuzzy set theory; genetic algorithm; information retrieval; intelligent control; intelligent decision making; intelligent information processing; intelligent recognition; intelligent robotics; machine learning; natural language & machine translation; neural networks;...
One of the basic assumptions in traditional machine learning is that it requires training and test data be under the same distribution. However, in image classification, this assumption often does not hold, since image labels are not as sufficient as text ones. In this paper, we propose to use labeled images from relevant but different categories to take the role of training data for estimating a...
In this paper, we present an automatic terminology extraction approach for Chinese multi-word terms. In this term extraction system, besides five linguistic rules acquired from an available term list by some machine learning methods, two statistical strategies are involved: a termhood measure based on the term distribution variation, and a unithood measure adopting the left and right entropy method...
The central problem for many applications in Information retrieval is ranking. Learning to rank has been considered as a promising approach for addressing the issue. In this paper, we focus on applying learning to rank to document retrieval, particularly the approach of using multiple hyperplanes to perform the task. Ranking SVM (RSVM) is a typical method of learning to rank. We point out that although...
One of the basic characteristics in human problem solving is the ability to conceptualize the world at different granularities and translate from one abstraction level to the others easily. But so far computers can only deal with one abstraction level in problem solving generally. It seems important to develop new techniques which will in some way enable the computers to represent the world at different...
DNA-binding proteins play an important role in various intra- and extra-cellular activities. The key in the protein is DNA-binding region also called DNA-binding domain (DBD). However, it is hard to search the DBDs by means of homology search or hidden Markov models because of a wide variety of the sequences. In this work, we develop a kernel-based machine learning method by combination of multiple...
We are living in a world that includes both digital and real objects. Sensors provide a bridge between these two spaces. Is it possible to use machine learning to acquire the sensor signals and turn them into useful knowledge about people??s locations, actions and behavior? Is it possible to then guide people in the physical world through intelligent inference using the knowledge learned? In this...
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