Multiple Instance Learning (MIL) is concerned with learning from sets (bags) of feature vectors (instances), where the individual instance labels are ambiguous. In MIL it is often assumed that positive bags contain at least one instance from a so-called concept in instance space, whereas negative bags only contain negative instances. The classes in a MIL problem are therefore not treated in the...

Shrimp farming has traditionally served as a crucial source of seafood and revenue for coastal countries. However, with the rapid development society, conventional small-scale manual shrimp can no longer meet increasing demand growth. As result, it is imperative to continuously develop automation technology efficient large-scale farming. Smart represents an innovative application advanced techn...

We propose a multi-cue based approach for recognizing human actions in still images, where relevant object regions are discovered and utilized in a weakly supervised manner. Our approach does not require any explicitly trained object detector or part/attribute annotation. Instead, a multiple instance learning approach is used over sets of object hypotheses in order to represent objects relevant...

In this paper we present a probabilistic formalization of the instance-based learning approach. In our Bayesian framework, moving from the construction of an explicit hypothesis to a data-driven instance-based learning approach, is equivalent to averaging over all the (possibly innnitely many) individual models. The general Bayesian instance-based learning framework described in this paper can ...

In this paper, we propose to solve multiple instance learning problems using a dissimilarity representation of the objects. Once the dissimilarity space has been constructed, the problem is turned into a standard supervised learning problem that can be solved with a general purpose supervised classifier. This approach is less restrictive than kernelbased approaches and therefore allows for the ...

In supervised learning, a training set consisting of labeled instances is used by a learning algorithm for generating a model (classifier) that is subsequently employed for deciding the class label of new instances (for generalization). Characteristics of the training set, such as presence of noisy instances and size, influence the learning algorithm and affect generalization performance. This ...

We describe a new instance-based learning algorithm called the Boundary Forest (BF) algorithm, that can be used for supervised and unsupervised learning. The algorithm builds a forest of trees whose nodes store previously seen examples. It can be shown data points one at a time and updates itself incrementally, hence it is naturally online. Few instance-based algorithms have this property while...

Disfluent speech adds to the difficulty of processing spoken language utterances. In this paper we concentrate on identifying one disfluency phenomenon: fragmented words. Our data, from the Spoken Dutch Corpus, samples nearly 45,000 sentences of human discourse, ranging from spontaneous chat to media broadcasts. We classify each lexical item in a sentence either as a completely or an incomplete...

Metonymy recognition is generally approached with complex algorithms that rely heavily on the manual annotation of training and test data. This paper will relieve this complexity in two ways. First, it will show that the results of the current learning algorithms can be replicated by the ‘lazy’ algorithm of Memory-Based Learning. This approach simply stores all training instances to its memory ...

This paper presents an investigation into the combination of different classifiers for toxicity prediction. These classification methods involved in generating classifiers for combination are chosen in terms of their representability and diversity which include the Instance-based Learning algorithm (IBL), Decision Tree learning algorithm (DT), Repeated Incremental Pruning to Produce Error Reduc...