113,342 research outputs found
Regularized Regression Problem in hyper-RKHS for Learning Kernels
This paper generalizes the two-stage kernel learning framework, illustrates
its utility for kernel learning and out-of-sample extensions, and proves
{asymptotic} convergence results for the introduced kernel learning model.
Algorithmically, we extend target alignment by hyper-kernels in the two-stage
kernel learning framework. The associated kernel learning task is formulated as
a regression problem in a hyper-reproducing kernel Hilbert space (hyper-RKHS),
i.e., learning on the space of kernels itself. To solve this problem, we
present two regression models with bivariate forms in this space, including
kernel ridge regression (KRR) and support vector regression (SVR) in the
hyper-RKHS. By doing so, it provides significant model flexibility for kernel
learning with outstanding performance in real-world applications. Specifically,
our kernel learning framework is general, that is, the learned underlying
kernel can be positive definite or indefinite, which adapts to various
requirements in kernel learning. Theoretically, we study the convergence
behavior of these learning algorithms in the hyper-RKHS and derive the learning
rates. Different from the traditional approximation analysis in RKHS, our
analyses need to consider the non-trivial independence of pairwise samples and
the characterisation of hyper-RKHS. To the best of our knowledge, this is the
first work in learning theory to study the approximation performance of
regularized regression problem in hyper-RKHS.Comment: 25 pages, 3 figure
Classification theorems for the C*-algebras of graphs with sinks
We consider graphs E which have been obtained by adding one or more sinks to
a fixed directed graph G. We classify the C*-algebra of E up to a very strong
equivalence relation, which insists, loosely speaking, that C*(G) is kept
fixed. The main invariants are vectors W_E : G^0 -> N which describe how the
sinks are attached to G; more precisely, the invariants are the classes of the
W_E in the cokernel of the map A-I, where A is the adjacency matrix of the
graph G.Comment: 16 pages, uses XY-pi
Multiclass Learning with Simplex Coding
In this paper we discuss a novel framework for multiclass learning, defined
by a suitable coding/decoding strategy, namely the simplex coding, that allows
to generalize to multiple classes a relaxation approach commonly used in binary
classification. In this framework, a relaxation error analysis can be developed
avoiding constraints on the considered hypotheses class. Moreover, we show that
in this setting it is possible to derive the first provably consistent
regularized method with training/tuning complexity which is independent to the
number of classes. Tools from convex analysis are introduced that can be used
beyond the scope of this paper
Classification of protein interaction sentences via gaussian processes
The increase in the availability of protein interaction studies in textual format coupled with the demand for easier access to the key results has lead to a need for text mining solutions. In the text processing pipeline, classification is a key step for extraction of small sections of relevant text. Consequently, for the task of locating protein-protein interaction sentences, we examine the use of a classifier which has rarely been applied to text, the Gaussian processes (GPs). GPs are a non-parametric probabilistic analogue to the more popular support vector machines (SVMs). We find that GPs outperform the SVM and na\"ive Bayes classifiers on binary sentence data, whilst showing equivalent performance on abstract and multiclass sentence corpora. In addition, the lack of the margin parameter, which requires costly tuning, along with the principled multiclass extensions enabled by the probabilistic framework make GPs an appealing alternative worth of further adoption
StackInsights: Cognitive Learning for Hybrid Cloud Readiness
Hybrid cloud is an integrated cloud computing environment utilizing a mix of
public cloud, private cloud, and on-premise traditional IT infrastructures.
Workload awareness, defined as a detailed full range understanding of each
individual workload, is essential in implementing the hybrid cloud. While it is
critical to perform an accurate analysis to determine which workloads are
appropriate for on-premise deployment versus which workloads can be migrated to
a cloud off-premise, the assessment is mainly performed by rule or policy based
approaches. In this paper, we introduce StackInsights, a novel cognitive system
to automatically analyze and predict the cloud readiness of workloads for an
enterprise. Our system harnesses the critical metrics across the entire stack:
1) infrastructure metrics, 2) data relevance metrics, and 3) application
taxonomy, to identify workloads that have characteristics of a) low sensitivity
with respect to business security, criticality and compliance, and b) low
response time requirements and access patterns. Since the capture of the data
relevance metrics involves an intrusive and in-depth scanning of the content of
storage objects, a machine learning model is applied to perform the business
relevance classification by learning from the meta level metrics harnessed
across stack. In contrast to traditional methods, StackInsights significantly
reduces the total time for hybrid cloud readiness assessment by orders of
magnitude
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