6590646
Emergent Specialization in Swarm Systems
Distributed learning is the learning process of multiple autonomous agents in a varying environment, where each agent has only partial information about the global task. In this paper, we investigate the influence of different reinforcement signals (local and global) and team diversity (homogeneous and heterogeneous agents) on the learned solutions. We compare the learned solutions with those obtained by systematic search in a simple case study in which pairs of agents have to collaborate in order to solve the task without any explicit communication. The results show that policies which allow teammates to specialize find an adequate diversity of the team and, in general, achieve similar or better performances than policies which force homogeneity. However, in this specific case study, the achieved team performances appear to be independent of the locality or globality of the reinforcement signal.
8054797
Infinite Ensemble Learning with Support Vector Machines
Ensemble learning algorithms such as boosting can achieve better performance by averaging over the predictions of base hypotheses. However, existing algorithms are limited to combining only a finite number of hypotheses, and the generated ensemble is usually sparse. It is not clear whether we should construct an ensemble classifier with a larger or even infinite number of hypotheses. In addition, constructing an infinite ensemble itself is a challenging task. In this paper, we formulate an infinite ensemble learning framework based on SVM. The framework can output an infinite and nonsparse ensemble, and can be used to construct new kernels for SVM as well as to interpret some existing ones. We demonstrate the framework with a concrete application, the stump kernel, which embodies infinitely many decision stumps. The stump kernel is simple, yet powerful. Experimental results show that SVM with the stump kernel is usually superior than boosting, even with noisy data.
5082292
Survival prediction of diffuse large-B-cell lymphoma based on both clinical and gene expression information
MOTIVATION
It is important to predict the outcome of patients with diffuse large-B-cell lymphoma after chemotherapy, since the survival rate after treatment of this common lymphoma disease is <50%. Both clinically based outcome predictors and the gene expression-based molecular factors have been proposed independently in disease prognosis. However combining the high-dimensional genomic data and the clinically relevant information to predict disease outcome is challenging.


RESULTS
We describe an integrated clinicogenomic modeling approach that combines gene expression profiles and the clinically based International Prognostic Index (IPI) for personalized prediction in disease outcome. Dimension reduction methods are proposed to produce linear combinations of gene expressions, while taking into account clinical IPI information. The extracted summary measures capture all the regression information of the censored survival phenotype given both genomic and clinical data, and are employed as covariates in the subsequent survival model formulation. A case study of diffuse large-B-cell lymphoma data, as well as Monte Carlo simulations, both demonstrate that the proposed integrative modeling improves the prediction accuracy, delivering predictions more accurate than those achieved by using either clinical data or molecular predictors alone.
27925816
A fuzzy approach to the multiobjective transportation problem
The aim of this paper is to present a fuzzy compromise programming approach to multiobjective transportation problems. A characteristic feature of the approach proposed is that various objectives are synthetically considered with the marginal evaluation for individual objectives and the global evaluation for all objectives. The decision-maker’s preference is taken into account by his/her assigning the weights of objectives. With the global evaluation for all objectives, a compromise programming model is formulated. This model covers a wide spectrum of methods with Zimmermann’s fuzzy programming approach essentially equivalent to one of its special cases. Using ordinary optimization technique, we solve the fuzzy compromise programming model to obtain a non-dominated compromise solution at which the synthetic membership degree of the global evaluation for all objectives is maximum. A numerical example is given to demonstrate the efficiency of the proposed fuzzy compromise programming approach. 
 
 
Scope and purpose 
 
In many real-world situations for transportation problems, decisions are often made in the presence of multiple, conflicting, incommensurate objectives. Intensive investigations on multiobjective linear transportation problem have been made. Among them Zimmermann’s fuzzy programming appears to be an ideal approach for obtaining the optimal compromise solution to a multiobjective transportation problem. However, due to the ease of computation, the aggregate operator used in Zimmermann’s fuzzy programming is the “min” operator, which does not guarantee a non-dominated solution. In this paper, we propose a new fuzzy compromise programming approach to multiobjective transportation problems. It is shown that the approach proposed can give a compromise solution that is not only non-dominated but also optimal in a certain sense. The proposed approach is robust enough to cover a wide spectrum of methods. Zimmermann’s fuzzy programming approach is essentially equivalent to one of the proposed method’s special cases.
207159693
PAC model-free reinforcement learning
For a Markov Decision Process with finite state (size S) and action spaces (size A per state), we propose a new algorithm---Delayed Q-Learning. We prove it is PAC, achieving near optimal performance except for Õ(SA) timesteps using O(SA) space, improving on the Õ(S2 A) bounds of best previous algorithms. This result proves efficient reinforcement learning is possible without learning a model of the MDP from experience. Learning takes place from a single continuous thread of experience---no resets nor parallel sampling is used. Beyond its smaller storage and experience requirements, Delayed Q-learning's per-experience computation cost is much less than that of previous PAC algorithms.
6768077
A parallel multiresolution volume rendering algorithm for large data visualization
We present a new parallel multiresolution volume rendering algorithm for visualizing large data sets. Using the wavelet transform, the raw data is first converted to a multiresolution wavelet tree. To eliminate the data dependency between processors at run-time, and achieve load-balanced rendering, we design a novel algorithm to partition the tree and distribute the data along a hierarchical space-filling curve with error-guided bucketization. Further optimization is achieved by storing reconstructed data at pre-selected tree nodes for each processor based on the available storage resources to reduce the overall wavelet reconstruction cost. At run time, the wavelet tree is first traversed according to the user-specified error tolerance. Data blocks of different resolutions that satisfy the error tolerance are then decompressed and rendered to compose the final image in parallel. Experimental results showed that our algorithm can reduce the run-time communication cost to a minimum and ensure a well-balanced workload among processors when visualizing gigabytes of data with arbitrary error tolerances.
21164331
Optimization model of internet protocols
Layered architecture is one of the most fundamental and influential structures of network design. Can we integrate the various protocol layers into a single coherent theory by regarding them as carrying out an asynchronous distributed primal-dual computation over the network to implicitly solve a global optimization problem? Different layers iterate on different subsets of the decision variables using local information to achieve individual optimalities, but taken together, these local algorithms attempt to achieve a global objective. Such a theory will expose the interconnection between protocol layers and can be used to study rigorously the performance tradeoff in protocol layering as different ways to distribute a centralized computation. In this talk, we describe some preliminary work towards this goal and discuss some of the difficulties of this approach.