We describe and evaluate a new method of automatic seed word selection for unsupervised sentiment classification of product reviews in Chinese. The whole method is unsupervised and does not require any annotated training data; it only requires information about commonly occurring negations and adverbials. Unsupervised techniques are promising for this task since they avoid problems of domain-de...

Classification is an important task in data analysis and learning. Classification can be performed using supervised or unsupervised methods. From the unsupervised point of view, Formal Concept Analysis (FCA) can be used for such a task in an efficient and well-founded way. From the supervised point of view, emerging patterns rely on pattern mining and can be used to characterize classes of obje...

Remote sensing is a promising technology that finds as diverse applications as defence, urbanplanning, healthcare, and environmental management. Collecting countrywide statistics of cropyield is one of the main tasks of remote sensing. Acquiring and processing very high-resolution(VHR) satellite images are means accomplishing this task. Processing these remotely sensed(RS) image...

Classification methods are commonly divided into two categories: unsupervised and supervised. Unsupervised methods have the ability to discover new classes by grouping data into clusters or tree structures without using the class labels, but they carry the risk of producing noninterpretable results. On the other hand, supervised methods always find decision rules that discriminate samples with ...

the coastal region of chabahar gulf with 1113.7km2 area has been located in the southeast of iran. in this research, satellite images of etm+ (2004) were used in order to zoning of sedimentary environments in this gulf. methodology of this research was baesd on synthesis of supervised and unsupervised classifications in gis and envi softwares. based on studies and calculation of oif index, the ...

In supervised learning scenarios, feature selection has been studied widely in the literature. Selecting features in unsupervised learning scenarios is a much harder problem, due to the absence of class labels that would guide the search for relevant information. And, almost all of previous unsupervised feature selection methods are “wrapper” techniques that require a learning algorithm to eval...

This paper describes a proposed automatic language accent identification system based on phoneme class trajectory models. Our focus is to preserve discriminant information of the spectral evolution that belong to each accent. Here, we describe two classification schemes based on stochastic trajectory models; supervised and unsupervised classification. For supervised classification, we assume te...

In this article we extend the (recently published) unsupervised information theoretic vector quantization approach based on the Cauchy–Schwarz-divergence for matching data and prototype densities to supervised learning and classification. In particular, first we generalize the unsupervised method to more general metrics instead of the Euclidean, as it was used in the original algorithm. Thereaf...

This paper presents a new hybrid learning algorithm for unsupervised classification tasks. We combined Fuzzy c-means learning and the supervised version of Minimerror to develop a hybrid incremental strategy allowing unsupervised classifications. We applied this new approach to a real-world database in order to know if the information contained in unlabeled signals of a Geographic Information S...