**Consilience**

We describe an apparently new measure of multivariate goodness-of-fit between sets of quantitative results from a model (simulation, analytical, or multiple regression), paired with those observed under corresponding conditions from the system being modeled. Our approach returns a single, integrative measure, even though it can accommodate complex systems that produce responses of M types. For each response-type, the goodness-of-fit measure, which we label ‘Consilience’ (C), is maximally 1, for perfect fit; near 0 for the large-sample case (number of pairs, N, more than about 25) in which the modeled series is a random sample from a quasi-normal distribution with the same mean and variance as that of the observed series (null model); and, less than 0, toward minus-infinity, for progressively worse fit. In addition, lack-of-fit for each response-type can be apportioned between systematic and non-systematic (unexplained) components of error. Finally, for statistical assessment of models relative to the equivalent null model, we offer provisional estimates of critical C vs. N, and of critical joint-C vs. N and M, at various levels of Pr(type-I error). Application of our proposed methodology requires only MS Excel (2003 or later); we provide Excel XLS and XLSX templates that afford semi-automatic computation for systems involving up to M = 5 response types, each represented by up to N = 1000 observed-and-modeled result pairs. N need not be equal, nor response pairs in complete overlap, over M. … **Infinite Layer Networks (ILN)**

Infinite Layer Networks (ILN) have recently been proposed as an architecture that mimics neural networks while enjoying some of the advantages of kernel methods. ILN are networks that integrate over infinitely many nodes within a single hidden layer. It has been demonstrated by several authors that the problem of learning ILN can be reduced to the kernel trick, implying that whenever a certain integral can be computed analytically they are efficiently learnable. In this work we give an online algorithm for ILN, which avoids the kernel trick assumption. More generally and of independent interest, we show that kernel methods in general can be exploited even when the kernel cannot be efficiently computed but can only be estimated via sampling. We provide a regret analysis for our algorithm, showing that it matches the sample complexity of methods which have access to kernel values. Thus, our method is the first to demonstrate that the kernel trick is not necessary as such, and random features suffice to obtain comparable performance. … **Data-as-a-Service (DaaS)**

Data as a Service, or DaaS, is a cousin of software as a service. Like all members of the “as a Service” (aaS) family, DaaS is based on the concept that the product, data in this case, can be provided on demand to the user regardless of geographic or organizational separation of provider and consumer. Additionally, the emergence of service-oriented architecture (SOA) has rendered the actual platform on which the data resides also irrelevant. This development has enabled the recent emergence of the relatively new concept of DaaS. Data provided as a service was at first primarily used in Web mashups, but now is being increasingly employed both commercially and, less commonly, within organisations such as the UN. …

# If you did not already know

**20**
*Tuesday*
Feb 2018

Posted What is ...

in
Advertisements