Pedestrian detection in still image should handle the large appearance and stance variations arising from the articulated structure, various clothing of human as well as viewpoints. In this paper, we address this problem from a view which utilizes multiple instances to represent the variations in multiple instance learning (MIL) framework. Specifically, logistic multiple instance boost (LMIBoos...

the ever severe dynamic competitive environment has led to increasing complexity of strategic decision making in giant organizations. strategy formulation is one of basic processes in achieving long range goals. since, in ordinary methods considering all factors and their significance in accomplishing individual goals are almost impossible. here, a new approach based on clustering method is pro...

The global pattern mining step in existing pattern-based hierarchical clustering algorithms may result in an unpredictable number of patterns. In this paper, we propose IDHC, a pattern-based hierarchical clustering algorithm that builds a cluster hierarchy without mining for globally significant patterns. IDHC allows each instance to "vote" for its representative size-2 patterns in a way that e...

Clustering data into meaningful groups is one of most important tasks of both artificial intelligence and data mining. In general, clustering methods are considered unsupervised. However, in recent years, so-named constraints become more popular as means of incorporating additional knowledge into clustering algorithms. Over the last years, a number of clustering algorithms employing different t...

Clustering, classification, and regression, are three major research topics in machine learning. So far, much work has been conducted in solving multiple instance classification and multiple instance regression problems, where supervised training patterns are given as bags and each bag consists of some instances. But the research on unsupervised multiple instance clustering is still limited . T...

This paper describes a new topological map dedicated to clustering under instance-level constraints. In general, traditional clustering is used in an unsupervised manner. However, in some cases, background information about the problem domain is available or imposed in the form of constraints, in addition to data instances. In this context, we modify the popular SOM algorithm to take these cons...

Automated and accurate classification of Whole Slide Image (WSI) is great significance for the early diagnosis treatment cancer, which can be realized by Multi-Instance Learning (MIL). However, current MIL method easily suffers from over-fitting due to weak supervision slide-level labels. In addition, it difficult distinguish discriminative instances in a WSI bag absence pixel-level annotations...

Constrained clustering investigates how to incorporate domain knowledge in the clustering process. The domain knowledge takes the form of constraints that must hold on the set of clusters. We consider instance level constraints, such as must-link and cannot-link. This type of constraints has been successfully used in popular clustering algorithms, such as k-means and hierarchical agglomerative ...

In instance-based classifiers, there is a need for storing a large number of samples as training set. In this work, we propose an instance reduction method based on hyperrectangle clustering, called Instance Reduction Algorithm using Hyperrectangle Clustering (IRAHC). IRAHC removes non-border (interior) instances and keeps border and near border ones. This paper presents an instance reduction p...