nils-herrmann/clean_scirepeval huggingface dataset:
Leaf Image Retrieval with Shape Features

In this paper we present an efficient two-step approach of using a shape characterization function called centroid-contour distance curve and the object eccentricity (or elongation) for leaf image retrieval. Both the centroid-contour distance curve and the eccentricity of a leaf image are scale, rotation, and translation invariant after proper normalizations. In the frist step, the eccentricity is used to rank leaf images, and the top scored images are further ranked using the centroid-contour distance curve together with the eccentricity in the second step. A thinning-based method is used to locate start point(s) for reducing the matching time. Experimental results show that our approach can achieve good performance with a reasonable computational complexity.
Hidden Markov random field based approach for off-line handwritten Chinese character recognition

This paper presents a hidden Markov mesh random field (HMMRF) based approach for off-line handwritten Chinese characters recognition using statistical observation sequences embedded in the strokes of a character. Due to a large set of Chinese characters and many different writing styles, the recognition of handwritten Chinese characters is very challenging. In our approach, the binary image is first normalized by a nonlinear shape normalization scheme to adjust the width, length, and the correlation of strokes. Two types of stroke-based features are then extracted to represent the observation sequence. The estimation of model parameters and state sequence decoding algorithms are also discussed in the paper. Experimental results on 470 isolated handwritten Chinese characters demonstrate the effectiveness of our approach.
Content-based image retrieval using block-constrained fractal coding and nona-tree decomposition

Fractal coding has been proved useful for image compression. In fractal coding, an image is represented by a number of self-transformations (fractal code) by which an approximation of the original image can be reconstructed. The authors present a block-constrained fractal coding scheme and a nona-tree decomposition based matching strategy for content-based image retrieval. In the coding scheme, an image is partitioned into non-overlapped blocks with a size close to that of a query iconic image. The fractal code is generated for each block independently. In the similarity measure of the fractal code, an improved nona-tree decomposition scheme is adopted to avoid matching the fractal code globally in order to reduce computational complexity. The experimental results show that the authors' coding scheme and matching strategy are useful for image retrieval, and compare favourably with other two methods tested in terms of storage usage and computing time.
Document image matching based on component blocks

Document image matching is the key technique for document registration and retrieval. A new matching algorithm based on the document component block list and component block tree is proposed. Our method can effectively make use of the local information of each page block and the global information of page layout, while it is also robust to image distortion, filled-in text, and noises. This algorithm is then refined and applied to automatic data extraction of column forms. A demonstrating software package has been developed.
Block-Constrained Fractal Coding Scheme for Image Retrieval

Fractal coding has been proved useful for image compression. In this paper, we present a block-constrained fractal coding scheme and a matching strategy for content-based image retrieval. In our coding scheme, an image is partitioned into non-overlap blocks of a size close to that of an iconic image. Fractal codes are generated for each block independently. In the similarity measure of fractal codes, an improved nona-tree decomposition scheme is adopted to avoid matching the fractal codes globally in order to reduce computational complexity. Our experimental results show that our approach is effective for image retrieval.
Approaching the post nonlinearity of blind mixtures by hybrid neural network

It is very difficult to approach the post nonlinearity of blind mixtures. The recent neural networks for separating the post nonlinear blind mixtures are limited to the diagonal nonlinearity. In this paper a hybrid neural network is proposed to separate the post nonlinearly mixed blind signals with cross-channel disturbance. This hybrid network consists of a new neural blind de-mixer for approximating the post nonlinearity and a common network for separating the predicted linear mixtures. The blind de-mixer is made up of two subnets, which in total produce a "weak" nonlinear operator and can approach relatively strong nonlinearity by parameter-tuning. A six-step batch learning algorithm based on the fixed-point algorithm and information backpropagation is deduced. Preliminary results on a blind signal separation problem of two sources and four different types of post nonlinearity indicate the effectiveness of our model.
Electrocardiogram compression using modulus maxima of wavelet transform

This paper presents a new ECG compression method for applications in both ECG transmission and ECG data storing. The local modulus maxims of the wavelet transform of an ECG signal indicate the locations of sharp variation points in the signal that carry the most important information. Although a signal cannot be exactly reconstructed from the modulus maxima of its wavelet transform, we can obtain a close approximation of the original signal from these modulus maxima. By ignoring small modulus maxima and encoding only the locations and amplitudes of the remaining modulus maxima and the coarse scale signal, we can compress ECG signal significantly. Considering different energy levels at different scales, suitable thresholds are determined to remove unnecessary modulus maxima, such as those generated by noise and artifacts. With a high compression ratio (CR) and a low percentage root-mean-square (PRD), our approach to ECG compression compares favorably with other methods tested.
A background-thinning based algorithm for separating connected handwritten digit strings

Most algorithms for segmenting connected handwritten digit strings are based on the analysis of the foreground pixel distributions and the features on the upper/lower contours of the image. A new approach is presented to segment connected handwritten two-digit strings based on the thinning of background regions. The algorithm first locates several feature points on the background skeleton of the digit image. Possible segmentation paths are then constructed by matching these feature points. With geometric property measures, these segmentation paths are ranked using fuzzy rules generated from a decision-tree approach. Finally, the top ranked segmentation paths are tested one by one by an optimized nearest neighbor classifier until one of these candidates is accepted based on an acceptance criterion. Experimental results on NIST special database 3 show that our approach can achieve a correct classification rate of 92.4% with only 4.7% of digit strings rejected, which compares favorably with the other techniques tested.
Image content classification using a block Kolmogorov complexity measure

Image content classification is a very important step in document image analysis and understanding, and page-segmentation-based document image compression. In this paper, we present an new approach to classifying image content using block Kolmogorov complexity (KC) measures. A binarized two-dimensional image is first partitioned into blocks and each block image is converted into a one-dimensional binary sequence using either horizontal or vertical scanning. The block complexities are then computed over the obtained binary sequences. An image is classified into one of two categories, textual or pictorial images, using two fuzzy rules with the mean value and the standard deviation of block complexities. Experimental results on eight Chinese/English textual images of different fonts and eight different pictorial images show that our approach is reliable in discriminating these two types of images. Moreover, the performance of our method, where a training process is not required, is comparable to that of a neural network technique.
Length estimation of digit strings using a neural network with structure-based features

Accurate length estimation is very helpful for the suc- cessful segmentation and recognition of connected digit strings, in particular, for an off-line recognition system. However, little work has been done in this area due to the difficulties involved. A length es- timation approach is presented as a part of our automatic off-line digit recognition system. The kernel of our approach is a neural network estimator with a set of structure-based features as the in- puts. The system outputs are a set of fuzzy membership grades reflecting the degrees of an input digit string of having different lengths. Experimental results on National Institute of Standards and Technology (NIST) Special Database 3 and other derived digit strings shows that our approach can achieve an about 99.4% cor- rect estimation if the best two estimations are considered.
An on-line adaptive neural network for speech recognition

In this paper, we present an on-line learning neural network model, Dynamic Recognition Neural Network (DRNN), for real-time speech recognition. The property of accumulative learning of the DRNN makes it very suitable for real-time speech recognition with on-line learning. A comparison between the DRNN and Hidden Markov Model (HMM) shows that the computational complexity of the former is lower than that of the latter in both training and recognition. Encouraging results are obtained when the DRNN is tested on a BUPT digit database (Mandarin) and on the on-line learning of twenty isolated English computer command words.
Multi-channel handwritten digit recognition using neural networks

Human recognition is much more robust than machine recognition in dealing with rotated and noisy patterns. In this paper, we present a multi-channel neural network (MCNN) approach for handwritten digit recognition based on the human recognition experience in the hope of achieving human-like performance. In this approach, three neural net work modules are trained individually by using three different set of features, intensity-based, rotation invariant, and noise deducted features. The outputs of these three modules are then combined by a combination neural network which is trained separately. Experimental results on a database of 1900 digit patterns written by 190 people show that a recognition rate of 89.5% is obtained on an independent test set that includes both the rotated and noisy data.
Fuzzy Algorithms: With Applications to Image Processing and Pattern Recognition

Fuzzy sets probability and fuzziness fuzzy models. Membership functions: heuristic selections clustering approaches adjustment and toning applications concluding remarks. Fuzzy clustering: clustering and fuzzy partition fuzzy c-means algorithm fuzzy cohonen clustering networks cluster validity and optimal fuzzy clustering applications concluding remarks. Fuzzy rules and defuzzification: rules based on experience learning from examples decision tree approach neural network approach minimization of fuzzy rules defuzzification and optimization applications concluding remarks. Fuzzy classifiers: fuzzy nearest neighbour classifier fuzzy multilayer perceptron fuzy decision trees fuzzy string matching applications concluding remarks.  voting schemes maximum poteriori probability Dempster-Shafer evidence theory trained perceptron neural networks applications concluding remarks.
zzification for Handwritt Numeral Recognition

In this paper, we present a technique to produce fuzzy rules based on the ID3 approach and to optimize de- fuzzification parameters by using a two-layer perceptron. The technique overcomes the difficulties in a conventional syntactic approach to handwritten Character recognition, including prob- lems of choosing a starting or reference point, scaling, and learning by machines. Our technique provides us with: a way to produce meaningful and simple fuzzy rules; a method to fuzzify ID3-derived rules to deal with uncertain, noisy, or fuzzy data; and a framework to incorporate fuzzy rules learned from the training data and those extracted from human recognition experience. Our experimental results on NIST Special Database 3 show that the technique out-performs the straight forward ID3 approach. Moreover, ID3-derived fuzzy rules can be combined with an optimized nearest neighbor classifier, which uses intensity features only, to achieve a better classification performance than either of the classifiers. The combined classifier achieves a correct classification rate of 98.6% on the test set.
ID3-derived fuzzy rules and optimized defuzzification for handwritten numeral recognition

Presents a technique to produce fuzzy rules based on the ID3 approach and to optimize defuzzification parameters by using a two-layer perceptron. The technique overcomes the difficulties in a conventional syntactic approach to handwritten character recognition, including problems of choosing a starting or reference point, scaling, and learning by machines. The authors' technique provides: a way to produce meaningful and simple fuzzy rules; a method to fuzzify ID3-derived rules to deal with uncertain, noisy, or fuzzy data; and a framework to incorporate fuzzy rules learned from the training data and those extracted from human recognition experience. The authors' experimental results on NIST Special Database 3 show that the technique out-performs the straight forward ID3 approach. Moreover, ID3-derived fuzzy rules can be combined with an optimized nearest neighbor classifier, which uses intensity features only, to achieve a better classification performance than either of the classifiers. The combined classifier achieves a correct classification rate of 98.6% on the test set.
Feature evaluation and selection based on an entropy measure with data clustering

We present a technique for evaluation and selection of features based on an entropy measure with 1-D K-means clustering of individual features. The technique compares favorably with a combinational selection technique based on the Euclidean distance separability measure in terms of computational requirement. Experiments on the handwritten numeral recognition problem using the multilayer perception classifier show that the technique can reliably evaluate features and successfully select those ones important for performing the classification using a system of reduced complexity with little degradation of the performance. The technique can also be used to discard the noise-corrupted features in order to increase the reliability of a classification system.
MLP classifiers: overtraining and solutions

Training a multi-layer perceptron (MLP) classifier is difficult to control and as a result its performance on unseen patterns is unpredicted. Overtraining is one of many problems in training an MLP classifier. In this paper, the author first discusses the overtraining problem based on an artificial two-input two-category classification problem. The author then suggests five solutions to the overtraining problem, which are supported by experimental results.
Image segmentation using fuzzy rules derived from K-means clusters

Image segmentation is one of the most important steps in computerized systems for analyzing geographic map images. We present a segmentation technique, based on fuzzy rules derived from the K-means clusters, that is aimed at achieving human-like performance. In this technique, the K-means clustering algorithm is first used to obtain mixed-class clusters of training examples, whose centers and variances are then used to determine membership functions. Based on the derived membership functions, fuzzy rules are 
learned from the K-means cluster centers. In the map image segmentation, we make use of three features, difference intensity, standard deviation, and a measure of the local contrast, to classify each pixel to the foreground, which consists of character and fine patterns, and to the background. A centroid defuzzification algorithm is adopted in the classification step. Experimental results on a database of 22 gray-scale map images show that the technique achieves good and reliable results, and is compared favorably with an adaptive thresholding method. By using K-means clustering, we can build a segmentation system of fewer rules that achieves a segmentation quality similar to that of using the uniformly distributed triangular membership functions with the fuzzy rules learned from all the training 
examples.
Separation of single- and double-touching handwritten numeral strings

To improve overall separation and classification performance for touching handwritten numeral strings, we have to deal with double-touching problems, which occur particularly for some combinations of digits. We propose a strategy to separate single- and double-touching numerical strings. The proposed technique includes: using two-cut links to deal with double-touching problems; using weighted geometric-property measures to rank both one- and two-cut links; and using a rejection scheme to reject poor links at both ranking and recognition stages. Two-cut link pairs that join the high-curvature points in inner contour(s) to those in the external contour are considered when all one-cut links that join the upper and the lower external contours fail to separate them. All combinations of one- and two-cut links are ranked according to weighted geometric-property measures, and the top links are chosen and tested by the recognition system. Experimental results on the National Institute of Standards and Technology database show that our system for solving both single- and double-touching problems compares favorably with the other techniques tested.
On Gaussian Expectation Propagation

In this short note we will re-derive the Gaussian expectation propagation (Gaussian EP) algorithm as presented in Minka (2001) and demonstrate an application of Gaussian EP to approximate multi-dimensional truncated Gaussians. 1 On Gaussian Distributions Here we will summarise some important equalities about the Gaussian density. A Gaussian density in Rn is defined by N (x;μ,6) := (2π)− 2 |6|− 1 2 exp ( − 2 (x− μ)T6−1 (x− μ) ) . (1.1) We will write x ∼ N (x;μ,6) to both denote x has a distribution P(x) and that the density of this distribution is given by (1.1). We will write N (x) as a shorthand for N (x; 0, I). For t ∈ R, we will denote the cumulative Gaussian distribution function by 8(t;μ, σ 2) which is defined by 8 ( t;μ, σ 2 ) = Px∼N (x;μ,σ 2) (x ≤ t) = ∫ t −∞ N ( x;μ, σ 2 ) dx . (1.2) Again, we write 8(t) as a shorthand for 8(t; 0, 1). We will write 〈 f (x)〉x∼P to denote the expectation of f over the random draw of x , that is 〈 f (x)〉x∼P := ∫ f (x)d P(x). The following results are given without proof; for a detailed derivation the reader is referred to Herbrich (2002). Linear transformation x ∼ N (x;μ,6) and y = Ax+ b⇒ y ∼ N ( y;Aμ+ b,A6AT ) . Convolutions Assume x ∼ N (x;μ,6) and y|x ∼ N (y;Ax,0) . Then x|y ∼ N ( x;9 ( AT0−1y+6−1μ ) ,9 ) , (1.3) y ∼ N ( x;Aμ,0 + A6AT ) , (1.4) where 9 := (AT0−1A+6−1)−1. Marginals Let us assume that a random vector x is composed such that x ∼ N ([ x1 x2 ] ; [ μ1 μ2 ] , [ 611 612 612 622 ]) .
Minimising the Kullback-Leibler Divergence

where φ(x) is known as the natural statistic of x and Z(θ) := ∫ exp(θTφ(x))dx ensures normalisation. The exponential family includes many known families of distributions including the Gaussian distribution. For example, in the Gaussian case, the natural statistic φ (x) is simply the vector of all first and second moments, φ (x) = (x1, . . . , xN , x2 1 , x1x2, . . . , xN xN−1, x 2 N ). Note that the expected natural statistic of pθ(x) is given in terms of the gradient of log(Z(θ)) w.r.t. θ , that is, ∇θ log (Z (θ)) = ∫ [
Poisson-Networks: A Model for Structured Poisson Processes

Modelling structured multivariate point process data has wide ranging applications like understanding neural activity, developing faster file access systems and learning dependencies among servers in large networks. In this paper, we develop the Poisson network model for representing multivariate structured Poisson processes. In our model each node of the network represents a Poisson process. The novelty of our work is that waiting times of a process are modelled by an exponential distribution with a piecewise constant rate function that depends on the event counts of its parents in the network in a generalised linear way. Our choice of model allows to perform exact sampling from arbitrary structures. We adopt a Bayesian approach for learning the network structure. Further, we discuss fixed point and sampling based approximations for performing inference of rate functions in Poisson networks.
The Informative Vector Machine: A Practical Probabilistic Alternative to the Support Vector Machine

We present a practical probabilistic alternative to the popular support vector machine (SVM). The algorithm is an approximation to a Gaussian process, and is probabilistic in the sense that it maintains the process variance that is implied by the use of a kernel function, which the SVM discards. We show that these variances may be tracked and made use of selection of an active set which gives a sparse representation for the model. For an active set size of d our algorithm exhibits O(dN) computational complexity and O(dN) storage requirements. It has already been shown that the approach is comptetive with the SVM in terms of performance and running time, here we give more details of the approach and demonstrate that kernel parameters may also be learned in a practical and effective manner.
LEARNING TO FIGHT

We apply reinforcement learning to the problem of finding good policies for a fighting agent in a commercial computer game. The learning agent is trained using the SARSA algorithm for on-policy learning of an action-value function represented by linear and neural network function approximators. We discuss the selection and construction of features, actions, and rewards as well as other design choices necessary to integrate the learning process into the game. The learning agent is trained against the built-in AI of the game with different rewards encouraging aggressive or defensive behaviour. We show that the learning agent finds interesting (and partly near optimal) policies in accordance with the reward functions provided. We also discuss the particular challenges arising in the application of reinforcement learning to the domain of computer games.
Invariant Pattern Recognition by Semi-Definite Programming Machines

Knowledge about local invariances with respect to given pattern transformations can greatly improve the accuracy of classification. Previous approaches are either based on regularisation or on the generation of virtual (transformed) examples. We develop a new framework for learning linear classifiers under known transformations based on semidefinite programming. We present a new learning algorithm— the Semidefinite Programming Machine (SDPM)—which is able to find a maximum margin hyperplane when the training examples are polynomial trajectories instead of single points. The solution is found to be sparse in dual variables and allows to identify those points on the trajectory with minimal real-valued output as virtual support vectors. Extensions to segments of trajectories, to more than one transformation parameter, and to learning with kernels are discussed. In experiments we use a Taylor expansion to locally approximate rotational invariance in pixel images from USPS and find improvements over known methods.