in instance-based learning, a training set is given to a classifier for classifying new instances. in practice, not all information in the training set is useful for classifiers. therefore, it is convenient to discard irrelevant instances from the training set. this process is known as instance reduction, which is an important task for classifiers since through this process the time for classif...

In instance-based learning the classification of a novel instance relies upon experience given in the form of similar instances whose labels are already known. Each of these instances can hence be seen as an individual piece of evidence. In this paper, we elaborate on issues concerning the representation and combination of such pieces of evidence. Particularly, we argue that the information pro...

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In this paper we propose a trainable method for extracting Chinese entity names and their relations. We view the entire problem as series of classification problems and employ memory-based learning (MBL) to resolve them. Preliminary results show that this method is efficient, flexible and promising to achieve better performance than other existing methods.

In this paper we describe our machine learning approach to the generation of referring expressions. As our algorithm we use memory-based learning. Our results show that in case of predicting the TYPE of the expression, having one general classifier gives the best results. On the contrary, when predicting the full set of properties of an expression, a combined set of specialized classifiers for ...

An application of relational instance-based learning to the complex task of expressive music performance is presented. We investigate to what extent a machine can automatically build ‘expressive profiles’ of famous pianists using only minimal performance information extracted from audio CD recordings by pianists and the printed score of the played music. It turns out that the machine-generated ...

We propose a generic, memory-based approach for the detection of implicit semantic roles. While state-of-the-art methods for this task combine hand-crafted rules with specialized and costly lexical resources, our models use large corpora with automated annotations for explicit semantic roles only to capture the distribution of predicates and their associated roles. We show that memory-based lea...

In this paper, we propose a Semi-Supervised MultipleInstance Learning (SSMIL) algorithm, and apply it to Localized ContentBased Image Retrieval(LCBIR), where the goal is to rank all the images in the database, according to the object that users want to retrieve. SSMIL treats LCBIR as a Semi-Supervised Problem and utilize the unlabeled pictures to help improve the retrieval performance. The comp...