2382558
Convex Hull Ensemble Machine for Regression and Classification
We propose a new ensemble algorithm called Convex Hull Ensemble Machine (CHEM). CHEM in Hilbert space is first developed and modified for regression and classification problems. We prove that the ensemble model converges to the optimal model in Hilbert space under regularity conditions. Empirical studies reveal that, for classification problems, CHEM has a prediction accuracy similar to that of boosting, but CHEM is much more robust with respect to output noise and never overfits datasets even when boosting does. For regression problems, CHEM is competitive with other ensemble methods such as gradient boosting and bagging.
7701199
Gradient LASSO for feature selection
LASSO (Least Absolute Shrinkage and Selection Operator) is a useful tool to achieve the shrinkage and variable selection simultaneously. Since LASSO uses the L1 penalty, the optimization should rely on the quadratic program (QP) or general non-linear program which is known to be computational intensive. In this paper, we propose a gradient descent algorithm for LASSO. Even though the final result is slightly less accurate, the proposed algorithm is computationally simpler than QP or non-linear program, and so can be applied to large size problems. We provide the convergence rate of the algorithm, and illustrate it with simulated models as well as real data sets.
206783748
Convex Hull Ensemble Machine
We propose a new ensemble algorithm called Convex Hull Ensemble Machine (CHEM). CHEM in Hilbert space is developed first and it is modified to regression and classification problems. Empirical studies show that in classification problems CHEM has similar prediction accuracy as AdaBoost, but CHEM is much more robust to output noise. In regression problems, CHEM works competitively with other ensemble methods such as Gradient Boost and Bagging.
36946020
Face Representation Method Using Pixel-to-Vertex Map (PVM) for 3D Model Based Face Recognition
3D model based approach for face recognition has been spotlighted as a robust solution under variant conditions of pose and illumination. Since a generative 3D face model consists of a large number of vertices, a 3D model based face recognition system is generally inefficient in computation time. In this paper, we propose a novel 3D face representation algorithm to reduce the number of vertices and optimize its computation time. Finally, we evaluate the performance of proposed algorithm with the Korean face database collected using a stereo-camera based 3D face capturing device.
47302704
On the performance of group key agreement protocols
Group key agreement (GKA) is a fundamental building block for securing peer group communication systems (GCS). Several group key agreement protocols were proposed in the past, all assuming an underlying group communication infrastructure. This paper presents a performance evaluation of 5 notable GKA protocols integrated with a reliable group communication system (Spread). They are: centralized group key distribution (CKD), Burmester-Desmedt (BD), Steer et al. (STR), group Diffie-Hellman GDH) and tree-based group Diffie-Hellman (TGDH).. We present concrete results obtained in experiments on local- and wide-area networks. Our analysis of these results offers insights into their relative scalability and practicality.
33400437
Facial Expression Recognition Using Active Appearance Model
This paper describes a facial expression recognition system based upon Active Appearance Model (AAM), which has been typically used for the face recognition task. Given that AAM has been also used in tracking the moving object, we thought it could be effective in recognizing the facial expressions of humans. Our results show that the performance of the facial expression recognition using AAM is reliably high when it combined with an enhanced Fisher classification model.
344302
New topologies for genetic search space
We propose three distance measures for genetic search space. One is a distance measure in the population space that is useful for understanding the working mechanism of genetic algorithms. Another is a distance measure in the solution space for K-grouping problems. This can be used for normalization in crossover. The third is a level distance measure for genetic algorithms, which is useful for measuring problem difficulty with respect to genetic algorithms. We show that the proposed measures are metrics and the measures are efficiently computed.
9445026
Efficient Uplink Scheduler Architecture of Subscriber Station in IEEE 802.16 System
The IEEE 802.16, broadband fixed wireless access standard, defines four service classes, USG, rtPS, nrtPS and BE on high speed wireless networks. To guarantee the QoS requirement of these classes, the subscriber station and base station require scheduling architecture and algorithm. However, the IEEE 802.16 does not define any scheduling architecture or algorithm, and the most existing scheduling mechanisms only focus on working at the BS. In this paper, we propose two types of scheduling architecture working at the SS. In the one-level scheduler, we use a flow queue and class queue by differentiating flows with their class priority. The two-level scheduler can provide more organized QoS service with complementing the one-level scheduler. Adapting these architectures makes scheduler efficiently control all types of traffic defined in the IEEE 802.16. In the proposed architecture, any scheduling algorithms such as SCFQ and EDF can be applied. We evaluate the proposed scheduling architecture by simulation. The results of the simulation show that our proposed architecture can use the bandwidth efficiently.