In multi-instance learning, the training set comprises labeled bags that are composed of unlabeled instances, and the task is to predict the labels of unseen bags. Through analyzing two famous multi-instance learning algorithms, this paper shows that many supervised learning algorithms can be adapted to multi-instance learning, as long as their focuses are shifted from the discrimination on the instances to the discrimination on the bags. Moreover, considering that ensemble learning paradigms can effectively enhance supervised learners, this paper proposes to build ensembles of multi-instance learners to solve multi-instance problems. Experiments on a real-world benchmark test show that ensemble learning paradigms can significantly enhance multi-instance learners, and the result achieved by EM-DD ensemble exceeds the best result on the benchmark test reported in literature.

In this paper, a hybrid learning approach named hybrid decision tree (HDT) is proposed. HDT simulates human reasoning using symbolic leaming to do qualitative analysis and using neurallearning to do subsequent quantitative analysis. It generates the trunk of a binary HDT according to the binary inormation gain r atio critetion in an instance space definde by only original unordered attributes. If unordered attributes cannot further distingguish training examples falling into a leaf node whose diversity is beyond the diversity-threshold, then the node is marked as a dummy node. After all those dummy nodes are marked, a speific feedforward neural netword namde FANNC that is trainde in an instance space definde by only original ordered attributes is exploited to accomplish the leaming task. Moreover, this paper distinguishes three kinds of inremental learning tasks. Two incremental leaming procedures designde for example-incremental learning with different storage requirements are provided, which enables HDT to deal gracefully with data sets where new data are freaquently appended. Also a hypothesis-driven constructive induction mechanism is provided, which enables HDT to generate compact concept descriptions.

At present there are many methods that could deal well with frontal view face recognition. However, most of them cannot work well when there is only one training image per person. In this paper, an extension of the eigenface technique, i.e. projection-combined principal component analysis, (PC) 2A, is proposed. (PC) 2A combines the original face image with its horizontal and vertical projections and then performs principal component analysis on the enriched version of the image. It requires less computational cost than the standard eigenface technique and experimental results show that on a gray-level frontal view face database where each person has only one training image, (PC)2A achieves 3-5% higher accuracy than the standard eigenface technique through using 10-15% fewer eigenfaces.

Ensemble learning algorithms train multiple component learners and then combine their predictions. In order to generate a strong ensemble, the component learners should be with high accuracy as well as high diversity. A popularly used scheme in generating accurate but diverse component learners is to perturb the training data with resampling methods, such as the bootstrap sampling used in bagging. However, such a scheme is not very effective on local learners such as nearest-neighbor classifiers because a slight change in training data can hardly result in local learners with big differences. In this paper, a new ensemble algorithm named Filtered Attribute Subspace based Bagging with Injected Randomness (FASBIR) is proposed for building ensembles of local learners, which utilizes multimodal perturbation to help generate accurate but diverse component learners. In detail, FASBIR employs the perturbation on the training data with bootstrap sampling, the perturbation on the input attributes with attribute filtering and attribute subspace selection, and the perturbation on the learning parameters with randomly configured distance metrics. A large empirical study shows that FASBIR is effective in building ensembles of nearest-neighbor classifiers, whose performance is better than that of many other ensemble algorithms.

In this paper an e cient face candidates selector is proposed for face detection tasks in still gray level images. The proposed method acts as a selective attentional mechanism. Eye-analogue segments at a given scale are discovered by 5nding regions which are roughly as large as real eyes and are darker than their neighborhoods. Then a pair of eye-analogue segments are hypothesized to be eyes in a face and combined into a face candidate if their placement is consistent with the anthropological characteristic of human eyes. The proposed method is robust in that it can deal with illumination changes and moderate rotations. A subset of the FERET data set and the BioID face database are used to evaluate the proposed method. The proposed face candidates selector is successful in 98.75% and 98.6% cases, respectively.