Reduced pattern training based on task decomposition using pattern distributor

Task Decomposition with Pattern Distributor (PD) is a new task decomposition method for multilayered feedforward neural networks. Pattern distributor network is proposed that implements this new task decomposition method. We propose a theoretical model to analyze the performance of pattern distributor network. A method named Reduced Pattern Training is also introduced, aiming to improve the performance of pattern distribution. Our analysis and the experimental results show that reduced pattern training improves the performance of pattern distributor network significantly. The distributor module’s classification accuracy dominates the whole network’s performance. Two combination methods, namely Cross-talk based combination and Genetic Algorithm based combination, are presented to find suitable grouping for the distributor module. Experimental results show that this new method can reduce training time and improve network generalization accuracy when compared to a conventional method such as constructive backpropagation or a task decomposition method such as Output Parallelism

Hierarchical incremental class learning with reduced pattern training

Hierarchical Incremental Class Learning (HICL) is a new task decomposition method that addresses the pattern classification problem. HICL is proven to be a good classifier but closer examination reveals areas for potential improvement. This paper proposes a theoretical model to evaluate the performance of HICL and presents an approach to improve the classification accuracy of HICL by applying the concept of Reduced Pattern Training (RPT). The theoretical analysis shows that HICL can achieve better classification accuracy than Output Parallelism [1]. The procedure for RPT is described and compared with the original training procedure. RPT reduces systematically the size of the training data set based on the order of sub-networks built. The results from four benchmark classification problems show much promise for the improved model

An incremental approach to MSE-based feature selection

Feature selection plays an important role in classification systems. Using classifier error rate as the evaluation function, feature selection is integrated with incremental training. A neural network classifier is implemented with an incremental training approach to detect and discard irrelevant features. By learning attributes one after another, our classifier can find directly the attributes that make no contribution to classification. These attributes are marked and considered for removal. Incorporated with a Minimum Squared Error (MSE) based feature ranking scheme, four batch removal methods based on classifier error rate have been developed to discard irrelevant features. These feature selection methods reduce the computational complexity involved in searching among a large number of possible solutions significantly. Experimental results show that our feature selection methods work well on several benchmark problems compared with other feature selection methods. The selected subsets are further validated by a Constructive Backpropagation (CBP) classifier, which confirms increased classification accuracy and reduced training cost

Task decomposition using pattern distributor

In this paper, we propose a new task decomposition method for multilayered feedforward neural networks, namely Task Decomposition with Pattern Distributor in order to shorten the training time and improve the generalization accuracy of a network under training. This new method uses the combination of modules (small-size feedforward network) in parallel and series, to produce the overall solution for a complex problem. Based on a “divide-and-conquer” technique, the original problem is decomposed into several simpler sub-problems by a pattern distributor module in the network, where each sub-problem is composed of the whole input vector and a fraction of the output vector of the original problem. These sub-problems are then solved by the corresponding groups of modules, where each group of modules is connected in series with the pattern distributor module and the modules in each group are connected in parallel. The design details and implementation of this new method are introduced in this paper. Several benchmark classification problems are used to test this new method. The analysis and experimental results show that this new method could reduce training time and improve generalization accuracy

Dynamical formation of stable irregular transients in discontinuous map systems

Stable chaos refers to the long irregular transients, with a negative largest Lyapunov exponent, which is usually observed in certain high-dimensional dynamical systems. The mechanism underlying this phenomenon has not been well studied so far. In this paper, we investigate the dynamical formation of stable irregular transients in coupled discontinuous map systems. Interestingly, it is found that the transient dynamics has a hidden pattern in the phase space: it repeatedly approaches a basin boundary and then jumps from the bundary to a remote region in the phase space. This pattern can be clearly visualized by measuring the distance sequences between the trajectory and the basin boundary. The dynamical formation of stable chaos originates from the intersection points of the discontinuous boundaries and their images. We carry out numerical experiments to verify this mechanism.Comment: 5 pages, 5 figure

Dual-frequency ferromagnetic resonance

We describe a new experimental technique to investigate coupling effects between different layers or modes in ferromagnetic resonance (FMR). Dual FMR frequencies are excited (2-8 GHz) simultaneously and detected selectively in a broadband RF circuit, using lock-in amplifier detection at separate modulation frequencies.Comment: 4 pages, 4 figures, accepted by "Review of Scientific Instruments", 200

A new genus and species of sabretooth, Oriensmilus liupanensis (Barbourofelinae, Nimravidae, Carnivora), from the middle Miocene of China suggests barbourofelines are nimravids, not felids

Since the early 2000s, a revival of a felid relationship for barbourofeline sabretooths has become popular due to recent discoveries of fragmentary fossils from Africa. According to this view, barbourofelines trace their common ancestor with felids through shared similarities in dental morphology going back to the early Miocene of Africa and Europe. However, whether or not such an idea is represented in the basicranial morphology, a conservative area of high importance in family-level relationships, is yet to be tested. A nearly complete skull of Oriensmilus liupanensis gen. and sp. nov. from the middle Miocene Tongxin Basin of northern China represents the most primitive known barbourofeline with an intact basicranial region, affording an opportunity to re-examine the relationship of felids and nimravines. We also present an update on East Asian records of barbourofelines. The new skull of Oriensmilus possesses a suite of characters shared with nimravines, such as the lack of an ossified (entotympanic) bullar floor, absence of an intrabullar septum, lack of a ventral promontorial process of the petrosal, presence of a small rostral entotympanic on the dorsal side of the caudal entotympanic, and a distinct caudal entry of the internal carotid artery and nerve that pierces the caudal entotympanic at the junction of the ossified and unossified caudal entotympanics. The absence of an ossified bullar floor in O. liupanensis and its presence in those from the middle Miocene of Sansan, France thus help to bracket the transition of this character, which must have happened in the early part of the middle Miocene. Spatial relationships between bullar construction and the middle ear configuration of the carotid artery in Oriensmilus strongly resemble those in nimravines but are distinctly different from felids and other basal feliforms. Despite the attractive notion that early barbourofelines arose from a Miocene ancestor that also gave rise to felids, the basicranial evidence argues against this view. http://zoobank.org/urn:http://lsid:zoobank.org:pub:2DE98DBC-4D02-4E18-9788-0B0D8587E73F