575 research outputs found
Random Search Plus: A more effective random search for machine learning hyperparameters optimization
Machine learning hyperparameter optimization has always been the key to improve model performance. There are many methods of hyperparameter optimization. The popular methods include grid search, random search, manual search, Bayesian optimization, population-based optimization, etc. Random search occupies less computations than the grid search, but at the same time there is a penalty for accuracy. However, this paper proposes a more effective random search method based on the traditional random search and hyperparameter space separation. This method is named random search plus. This thesis empirically proves that random search plus is more effective than random search. There are some case studies to do a comparison between them, which consists of four different machine learning algorithms including K-NN, K-means, Neural Networks and Support Vector Machine as optimization objects with three different size datasets including Iris flower, Pima Indians diabetes and MNIST handwritten dataset. Compared to traditional random search, random search plus can find a better hyperparameters or do an equivalent optimization as random search but with less time at most cases. With a certain hyperparameter space separation strategy, it can only need 10% time of random search to do an equivalent optimization or it can increase both the accuracy of supervised leanings and the silhouette coefficient of a supervised learning by 5%-30% in a same runtime as random search. The distribution of the best hyperparameters searched by the two methods in the hyperparameters space shows that random search plus is more global than random search. The thesis also discusses about some future works like the feasibility of using genetic algorithm to improve the local optimization ability of random search plus, space division of non-integer hyperparameters, etc
Local Search For SMT On Linear and Multilinear Real Arithmetic
Satisfiability Modulo Theories (SMT) has significant application in various
domains. In this paper, we focus on quantifier-free Satisfiablity Modulo Real
Arithmetic, referred to as SMT(RA), including both linear and non-linear real
arithmetic theories. As for non-linear real arithmetic theory, we focus on one
of its important fragments where the atomic constraints are multi-linear. We
propose the first local search algorithm for SMT(RA), called LocalSMT(RA),
based on two novel ideas. First, an interval-based operator is proposed to
cooperate with the traditional local search operator by considering the
interval information. Moreover, we propose a tie-breaking mechanism to further
evaluate the operations when the operations are indistinguishable according to
the score function. Experiments are conducted to evaluate LocalSMT(RA) on
benchmarks from SMT-LIB. The results show that LocalSMT(RA) is competitive with
the state-of-the-art SMT solvers, and performs particularly well on
multi-linear instances
An Efficient Data Analysis Method for Big Data using Multiple-Model Linear Regression
This paper introduces a new data analysis method for big data using a newly
defined regression model named multiple model linear regression(MMLR), which
separates input datasets into subsets and construct local linear regression
models of them. The proposed data analysis method is shown to be more efficient
and flexible than other regression based methods. This paper also proposes an
approximate algorithm to construct MMLR models based on
-estimator, and gives mathematical proofs of the correctness
and efficiency of MMLR algorithm, of which the time complexity is linear with
respect to the size of input datasets. This paper also empirically implements
the method on both synthetic and real-world datasets, the algorithm shows to
have comparable performance to existing regression methods in many cases, while
it takes almost the shortest time to provide a high prediction accuracy
Spectral flow, twisted modules and MLDE of quasi-lisse vertex algebras
We calculate the fusion rules among -twisted modules
at admissible levels. We derive a series MLDEs
for normalized characters of ordinary twisted modules of quasi-lisse vertex
algebras. Examples include affine VOAs of type at boundary
admissible level, admissible level , at boundary
admissible level , and -algebra with special value
. We also derive characters of some non-vacuum modules for affine VOA
of type at non-admissible level from spectral flow automorphism
Context-Dependent Diffusion Network for Visual Relationship Detection
Visual relationship detection can bridge the gap between computer vision and
natural language for scene understanding of images. Different from pure object
recognition tasks, the relation triplets of subject-predicate-object lie on an
extreme diversity space, such as \textit{person-behind-person} and
\textit{car-behind-building}, while suffering from the problem of combinatorial
explosion. In this paper, we propose a context-dependent diffusion network
(CDDN) framework to deal with visual relationship detection. To capture the
interactions of different object instances, two types of graphs, word semantic
graph and visual scene graph, are constructed to encode global context
interdependency. The semantic graph is built through language priors to model
semantic correlations across objects, whilst the visual scene graph defines the
connections of scene objects so as to utilize the surrounding scene
information. For the graph-structured data, we design a diffusion network to
adaptively aggregate information from contexts, which can effectively learn
latent representations of visual relationships and well cater to visual
relationship detection in view of its isomorphic invariance to graphs.
Experiments on two widely-used datasets demonstrate that our proposed method is
more effective and achieves the state-of-the-art performance.Comment: 8 pages, 3 figures, 2018 ACM Multimedia Conference (MM'18
SocioHub: An Interactive Tool for Cross-Platform Social Media Data Collection
Social media is inherently about connecting and interacting with others.
Different social media platforms have unique characteristics and user bases.
Moreover, people use different platforms for various social and entertainment
purposes. Analyzing cross-platform user behavior can provide insights into the
preferences and expectations of users on each platform. By understanding how
users behave and interact across platforms, we can build an understanding of
content consumption patterns, enhance communication and social interactions,
and tailor platform-specific strategies. We can further gather insights into
how users navigate and engage with their platforms on different devices. In
this work, we develop a tool SocioHub, which enables users to gather data on
multiple social media platforms in one place. This tool can help researchers
gain insights into different data attributes for users across social media
platforms such as Twitter, Instagram, and Mastodon. Keywords: Social Media
Platforms, Twitter, Instagram, Mastodon.Comment: 5 pages, 2 figure
Optimal Monotone Mean-Variance Problem in a Catastrophe Insurance Model
This paper explores an optimal investment and reinsurance problem involving
both ordinary and catastrophe insurance businesses. The catastrophic events are
modeled as following a compound Poisson process, impacting the ordinary
insurance business. The claim intensity for the ordinary insurance business is
described using a Cox process with a shot-noise intensity, the jump of which is
proportional to the size of the catastrophe event. This intensity increases
when a catastrophe occurs and then decays over time. The insurer's objective is
to maximize their terminal wealth under the Monotone Mean-Variance (MMV)
criterion. In contrast to the classical Mean-Variance (MV) criterion, the MMV
criterion is monotonic across its entire domain, aligning better with
fundamental economic principles. We first formulate the original MMV
optimization problem as an auxiliary zero-sum game. Through solving the
Hamilton-Jacobi-Bellman-Isaacs (HJBI) equation, explicit forms of the value
function and optimal strategies are obtained. Additionally, we provides the
efficient frontier within the MMV criterion. Several numerical examples are
presented to demonstrate the practical implications of the results
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