10981925
Geometrical Learning Algorithm for Multilayer Neural Networks in a Binary Field
A geometrical expansion learning algorithm for multilayer neural networks using unipolar binary neurons with integer connection weights, which guarantees convergence for any Boolean function, is introduced. Neurons in the hidden layer develop as necessary without supervision. In addition, the computational amount is much less than that of the backpropagation algorithm. >
27298271
The Rule-Based Approach to Reconfiguration of 2-D Processor Arrays
The authors propose to set up assignment rules based on the available interconnection resources of 2-D processor arrays. The reconfiguration of 2-D processor arrays is guided by the assignment rules, such that logical cells are always connected through the available interconnection resources. The advantage of the proposed approach is the easy adaptation to various system requirements, by adjusting assignment rules. The proposed reconfiguration algorithm can be extended to arrays with clustered faulty cells, overcoming the weakness of fixed domain approaches. >
38004947
On-Line Detection of Errors in Homogeneous Multiprocessor Systems
31990528
Diagnosing Multiple Bridge Faults in Baseline Multistage Interconnection Networks
20426558
The ANSER Project
6618252
Learning to Detect Conversation Focus of Threaded Discussions
In this paper we present a novel feature-enriched approach that learns to detect the conversation focus of threaded discussions by combining NLP analysis and IR techniques. Using the graph-based algorithm HITS, we integrate different features such as lexical similarity, poster trustworthiness, and speech act analysis of human conversations with feature-oriented link generation functions. It is the first quantitative study to analyze human conversation focus in the context of online discussions that takes into account heterogeneous sources of evidence. Experimental results using a threaded discussion corpus from an undergraduate class show that it achieves significant performance improvements compared with the baseline system.
26955217
On Temporal Aggregate Processing Based on Time Points
Temporal aggregation is an extension of conventional aggregate on the domain and range of aggregates to include the time concept. In this paper, we propose a new tree-based temporal aggregation processing method. The time complexity of the proposed method is better than those of the existing methods. The experimental results indicate that our proposed method ourperforms the existing methods.
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.
2033362
Bounding the cost of learned rules
Abstract In this article we approach one key aspect of the utility problem in explanation-based learning (EBL)—the expensive-rule problem —as an avoidable defect in the learning procedure. In particular, we examine the relationship between the cost of solving a problem without learning versus the cost of using a learned rule to provide the same solution, and refer to a learned rule as expensive if its use is more costly than the original problem solving from which it was learned. The key idea we explore is that expensiveness is inadvertently and unnecessarily introduced into learned rules by the learning algorithms themselves . This becomes a particularly powerful idea when combined with an analysis tool which identifies these hidden sources of expensiveness, and modifications of the learning algorithms which eliminate them. The result is learning algorithms for which the cost of learned rules is bounded by the cost of the problem solving that they replace.