15223911
Boosting with incomplete information
In real-world machine learning problems, it is very common that part of the input feature vector is incomplete: either not available, missing, or corrupted. In this paper, we present a boosting approach that integrates features with incomplete information and those with complete information to form a strong classifier. By introducing hidden variables to model missing information, we form loss functions that combine fully labeled data with partially labeled data to effectively learn normalized and unnormalized models. The primal problems of the proposed optimization problems with these loss functions are provided to show their close relationship and the motivations behind them. We use auxiliary functions to bound the change of the loss functions and derive explicit parameter update rules for the learning algorithms. We demonstrate encouraging results on two real-world problems --- visual object recognition in computer vision and named entity recognition in natural language processing --- to show the effectiveness of the proposed boosting approach.
38555873
Recognizing Human Action from Videos Using Histograms of Visual Words
We propose a new method for human action recognition from video sequences using histograms of visual words. Video sequences are represented by a novel "bag-of-words" representation, where each frame corresponds to a "word". The major difference between our model and previous "bag-of-words" models for recognition problems in computer vision is that, a "word" in our representation corresponds to a whole frame. The advantage of this representation is that the large-scale feature of a frame is better captured, which turns out to be important for recognition actions. We demonstrate our approach on two publicly available datasets. Our results are comparable to other state-of-the-art approaches for action recognition.
2358468
Human Action Recognition by Semilatent Topic Models
We propose two new models for human action recognition from video sequences using topic models. Video sequences are represented by a novel "bag-of-words" representation, where each frame corresponds to a "word". Our models differ from previous latent topic models for visual recognition in two major aspects: first of all, the latent topics in our models directly correspond to class labels; second, some of the latent variables in previous topic models become observed in our case. Our models have several advantages over other latent topic models used in visual recognition. First of all, the training is much easier due to the decoupling of the model parameters. Second, it alleviates the issue of how to choose the appropriate number of latent topics. Third, it achieves much better performance by utilizing the information provided by the class labels in the training set. We present action classification results on five different data sets. Our results are either comparable to, or significantly better than previously published results on these data sets.
9823069
Multiple Tree Models for Occlusion and Spatial Constraints in Human Pose Estimation
Tree-structured models have been widely used for human pose estimation, in either 2D or 3D. While such models allow efficient learning and inference, they fail to capture additional dependencies between body parts, other than kinematic constraints between connected parts. In this paper, we consider the use of multiple tree models, rather than a single tree model for human pose estimation. Our model can alleviate the limitations of a single tree-structured model by combining information provided across different tree models. The parameters of each individual tree model are trained via standard learning algorithms in a single tree-structured model. Different tree models can be combined in a discriminative fashion by a boosting procedure. We present experimental results showing the improvement of our approaches on two different datasets. On the first dataset, we use our multiple tree framework for occlusion reasoning. On the second dataset, we combine multiple deformable trees for capturing spatial constraints between non-connected body parts.
1878790
Unsupervised Discovery of Action Classes
In this paper we consider the problem of describing the action being performed by human figures in still images. We will attack this problem using an unsupervised learning approach, attempting to discover the set of action classes present in a large collection of training images. These action classes will then be used to label test images. Our approach uses the coarse shape of the human figures to match pairs of images. The distance between a pair of images is computed using a linear programming relaxation technique. This is a computationally expensive process, and we employ a fast pruning method to enable its use on a large collection of images. Spectral clustering is then performed using the resulting distances. We present clustering and image labeling results on a variety of datasets.
9978380
Learning a discriminative hidden part model for human action recognition
We present a discriminative part-based approach for human action recognition from video sequences using motion features. Our model is based on the recently proposed hidden conditional random field (hCRF) for object recognition. Similar to hCRF for object recognition, we model a human action by a flexible constellation of parts conditioned on image observations. Different from object recognition, our model combines both large-scale global features and local patch features to distinguish various actions. Our experimental results show that our model is comparable to other state-of-the-art approaches in action recognition. In particular, our experimental results demonstrate that combining large-scale global features and local patch features performs significantly better than directly applying hCRF on local patches alone.
6225988
A global optimal algorithm for class-dependent discretization of continuous data
This paper presents a new method to convert continuous variables into discrete variables for inductive machine learning. The method can be applied to pattern classification problems in machine learning and data mining. The discretization process is formulated as an optimization problem. We first use the normalized mutual information that measures the interdependence between the class labels and the variable to be discretized as the objective function, and then use fractional programming (iterative dynamic programming) to find its optimum. Unlike the majority of class-dependent discretization methods in the literature which only find the local optimum of the objective functions, the proposed method, OCDD, or Optimal Class-Dependent Discretization, finds the global optimum. The experimental results demonstrate that this algorithm is very effective in classification when coupled with popular learning systems such as C4.5 decision trees and Naive-Bayes classifier. It can be used to discretize continuous variables for many existing inductive learning systems.
35134245
Single Table Access Using Multiple Indexes: Optimization, Execution, and Concurrency Control Techniques
Many data base management systems' query optimizers choose at most one index for accessing the records of a table in a given query, even though many indexes may exist on the table. In spite of the fact that there are some systems which use multiple indexes, very little has been published about the concurrency control or query optimization implications (e.g., deciding how many indexes to use) of using multiple indexes. This paper addresses these issues and presents solutions to the associated problems. Techniques are presented for the efficient handling of record ID lists, elimination of some locking, and determination of how many and which indexes to use. The techniques are adaptive in the sense that the execution strategies may be modified at run-time (e.g., not use some indexes which were to have been used), if the assumptions made at optimization-time (e.g., about selectivities) turn out to be wrong. Opportunities for exploiting parallelism are also identified. A subset of our ideas have been implemented in IBM's DB2 V2R2 relational data base management system.
16798311
Parameter Inference of Cost-Sensitive Boosting Algorithms
Several cost-sensitive boosting algorithms have been reported as effective methods in dealing with class imbalance problem. Misclassification costs, which reflect the different level of class identification importance, are integrated into the weight update formula of AdaBoost algorithm. Yet, it has been shown that the weight update parameter of AdaBoost is induced so as the training error can be reduced most rapidly. This is the most crucial step of AdaBoost in converting a weak learning algorithm into a strong one. However, most reported cost-sensitive boosting algorithms ignore such a property. In this paper, we come up with three versions of cost-sensitive AdaBoost algorithms where the parameters for sample weight updating are induced. Then, their identification abilities on the small classes are tested on four “real world” medical data sets taken from UCI Machine Learning Database based on F-measure. Our experimental results show that one of our proposed cost-sensitive AdaBoost algorithms is superior in achieving the best identification ability on the small class among all reported cost-sensitive boosting algorithms.
58268816
Query Optimization in the IBM DB2 Family.
10165712
A Novel Secure Routing System in Overlay Environment
Routers are effective building blocks for Internet. It is almost impossible to modify router software only for applications. How to satisfy applications’ specific requirements of communication is a big problem. Now more and more systems begin to place application oriented routing service on overlay networks. This paper compares several overlay routing systems based on route number and brings forward a novel routing system based on trust model. System design principles are discussed and system simulation performance is analyzed. The results show that the system is able to provide secure routing service to applications in the case of vicious nodes.