Search results for "Intelligence"

Generic attribute deviation metric for assessing mesh simplification algorithm quality

2002

International audience; This paper describes an efficient method to compare two triangular meshes. Meshes considered here contain geometric features as well as other surface attributes such as material colors, texture, temperature, radiation, etc. Two deviation measurements are presented to assess the differences between two meshes. The first measurement, called geometric deviation, returns geometric differences. The second measurement , called attribute deviation, returns attribute differences regardless of the attribute type. In this paper we present an application of this method to the Mesh Simplification Algorithm (MSA) quality assessment according to the appearance attributes. This ass…

Self-learning inductive inference machines

1991

Self-knowledge is a concept that is present in several philosophies. In this article, we consider the issue of whether or not a learning algorithm can in some sense possess self-knowledge. The question is answered affirmatively. Self-learning inductive inference algorithms are taken to be those that learn programs for their own algorithms, in addition to other functions. La connaissance de soi est un concept qui se retrouve dans plusieurs philosophies. Dans cet article, les auteurs s'interrogent a savoir si un algorithme d' apprentissage peut dans une certaine mesure posseder la connaissance de soi. lis apportent une reponse positive a cette question. Les algorithmes d'inference inductive a…

Mobile phone data statistics as a dynamic proxy indicator in assessing regional economic activity and human commuting patterns

2020

A multi-step finite-state automaton for arbitrarily deterministic Tsetlin Machine learning

2021

Use of wavelet for image processing in smart cameras with low hardware resources

2013

International audience; Images from embedded sensors need digital processing to recover high-quality images and to extract features of a scene. Depending on the properties of the sensor and on the application, the designer fits together different algorithms to process images. In the context of embedded devices, the hardware supporting those applications is very constrained in terms of power consumption and silicon area. Thus, the algorithms have to be compliant with the embedded specifications i.e. reduced computational complexity and low memory requirements. We investigate the opportunity to use the wavelet representation to perform good quality image processing algorithms at a lower compu…

Irrelevant Features, Class Separability, and Complexity of Classification Problems

2011

In this paper, analysis of class separability measures is performed in attempt to relate their descriptive abilities to geometrical properties of classification problems in presence of irrelevant features. The study is performed on synthetic and benchmark data with known irrelevant features and other characteristics of interest, such as class boundaries, shapes, margins between classes, and density. The results have shown that some measures are individually informative, while others are less reliable and only can provide complimentary information. Classification problem complexity measurements on selected data sets are made to gain additional insights on the obtained results.

How Low Can Approximate Degree and Quantum Query Complexity Be for Total Boolean Functions?

2012

It has long been known that any Boolean function that depends on n input variables has both degree and exact quantum query complexity of Omega(log n), and that this bound is achieved for some functions. In this paper we study the case of approximate degree and bounded-error quantum query complexity. We show that for these measures the correct lower bound is Omega(log n / loglog n), and we exhibit quantum algorithms for two functions where this bound is achieved.

RNN- and LSTM-Based Soft Sensors Transferability for an Industrial Process

2021

The design and application of Soft Sensors (SSs) in the process industry is a growing research field, which needs to mediate problems of model accuracy with data availability and computational complexity. Black-box machine learning (ML) methods are often used as an efficient tool to implement SSs. Many efforts are, however, required to properly select input variables, model class, model order and the needed hyperparameters. The aim of this work was to investigate the possibility to transfer the knowledge acquired in the design of a SS for a given process to a similar one. This has been approached as a transfer learning problem from a source to a target domain. The implementation of a transf…

Attentional vs computational complexity measures in observing paintings

2009

Because of the great heterogeneity of subjects and styles, esthetic perception delineates a special and elusive field of research in vision, which represents an interesting challenge for cognitive science tools. With specific regard to the role of visual complexity, in this paper we present an experiment aimed to measure this dimension in a heterogeneous set of paintings. We compared perceived time complexity measures - based on a temporal estimation paradigm - with physical and statistical properties of the paintings, obtaining a strong correlation between psychological and computational results.

Convolutional Regression Tsetlin Machine: An Interpretable Approach to Convolutional Regression

2021

The Convolutional Tsetlin Machine (CTM), a variant of Tsetlin Machine (TM), represents patterns as straightforward AND-rules, to address the high computational complexity and the lack of interpretability of Convolutional Neural Networks (CNNs). CTM has shown competitive performance on MNIST, Fashion-MNIST, and Kuzushiji-MNIST pattern classification benchmarks, both in terms of accuracy and memory footprint. In this paper, we propose the Convolutional Regression Tsetlin Machine (C-RTM) that extends the CTM to support continuous output problems in image analysis. C-RTM identifies patterns in images using the convolution operation as in the CTM and then maps the identified patterns into a real…