Fuzzy hashes learn from data to improve file similarity detection.
problem Measuring similarity between files, especially malware.
method Learned fuzzy hashes using a minimax training framework.
result Learned fuzzy hashes outperform traditional methods for file similarity.
This paper introduces a novel real-time Fuzzy Supervised Learning with Binary Meta-Feature (FSL-BM) for big data classification task. The study of real-time algorithms addresses several major concerns, which are namely: accuracy, memory consumption, and ability to stretch assumptions and time complexity. Attaining a fa…
A new hashing framework learns multiple hash codes for each image to improve hash bucket search efficiency.
problem Existing hashing methods fail to handle complex image retrieval scenarios efficiently.
method Multiple Code Hashing (MCH) framework with deep reinforcement learning.
result Significant improvement in hash bucket search performance compared to single-code methods.
Hashing has been widely used for large-scale approximate nearest neighbor search because of its storage and search efficiency. Recent work has found that deep supervised hashing can significantly outperform non-deep supervised hashing in many applications. However, most existing deep supervised hashing methods adopt a …
Paper simulates LR fuzzy intervals with interval-valued cores.
problem Generating random fuzzy intervals with interval-valued cores.
method Developed algorithms for simulating LR fuzzy numbers with interval-valued cores.
result Numerically efficient algorithm for simulating fuzzy values.
A new hashing method improves accuracy by learning an orthogonal transform.
problem Improving hashing accuracy for similarity search.
method Procrustean Orthogonal Sparse Hashing (POSH) method.
result POSH outperforms state-of-the-art hashing methods.
In this paper, we have tried to apply the concepts of fuzzy sets to Lie groups and its relative concepts. First, we define a C1 fuzzy submanifold after reviewing C1−fuzzy manifold definition. In main section, we defined the Lie group and some its relative concepts such as fuzzy transformation group,…
Efficient Discrete Supervised Hashing improves cross-modal retrieval by preserving semantic correlations and reducing quantization error.
problem Challenges in preserving semantic correlations and reducing quantization error in cross-modal hashing for large-scale retrieval.
method Collective matrix factorization on heterogenous features and semantic embedding with class labels to learn hash codes efficiently.
result EDSH produces superior performance in both accuracy and scalability over existing methods.
FSDH is a fast supervised hashing method that outperforms existing methods.
problem Efficiently hashing large datasets for fast retrieval.
method FSDH uses a simple regression of class labels to hash codes, avoiding iterative discrete optimization.
result FSDH is significantly faster (12-151x) than existing methods like SDH and FastHash.
Fuzzy eIX method evolves classifiers for online data streams.
problem Handling time-varying classifiers in online data streams.
method Develops evolving Internal-eXternal Fuzzy granules for numerical data.
result Fuzzy eIX maintains high accuracy in dynamic scenarios.
New hashing method improves document retrieval precision.
problem Efficiently retrieving similar documents from large text databases.
method Pairwise supervised hashing with Bernoulli VAE and unbiased gradient estimator.
result Superior performance compared to existing methods.
FlexCMH learns effective hashing codes from weakly-paired data.
problem Cross-modal hashing assumes perfect correspondence between samples, which is unrealistic.
method FlexCMH uses clustering-based matching to find potential correspondence and jointly optimizes it with hashing functions.
result FlexCMH achieves significantly better results than state-of-the-art methods.
We generated a dataset of 200 GB with 10^9 features, to test our recent b-bit minwise hashing algorithms for training very large-scale logistic regression and SVM. The results confirm our prior work that, compared with the VW hashing algorithm (which has the same variance as random projections), b-bit minwise hashing i…
Hash codes are a very efficient data representation needed to be able to cope with the ever growing amounts of data. We introduce a random forest semantic hashing scheme with information-theoretic code aggregation, showing for the first time how random forest, a technique that together with deep learning have shown spe…
Hashing is a basic tool for dimensionality reduction employed in several aspects of machine learning. However, the perfomance analysis is often carried out under the abstract assumption that a truly random unit cost hash function is used, without concern for which concrete hash function is employed. The concrete hash f…
BioHash improves similarity search performance using sparse high-dimensional hash codes.
problem Improving similarity search performance in high-dimensional data.
method BioHash produces sparse high-dimensional hash codes through a data-driven approach based on synaptic plasticity.
result BioHash outperforms previous hashing methods in various similarity search tasks.
Minwise hashing is the standard technique in the context of search and databases for efficiently estimating set (e.g., high-dimensional 0/1 vector) similarities. Recently, b-bit minwise hashing was proposed which significantly improves upon the original minwise hashing in practice by storing only the lowest b bits of e…
DCFS predicts stock indices using deep learning and fuzzy systems.
problem Predicting stock indices with high-dimensional inputs.
method Bottom-up layer-by-layer design of DCFS, parameter sharing.
result DCFS models outperform weak estimators and real-world data.
A new hashing method handles complex multi-level labels.
problem Handling complex multi-level labels in cross-modal data retrieval.
method Derives a semantic ranking list from feature and label information, integrates semantic ranking into deep cross-modal hashing.
result RDCMH outperforms other methods in cross-modal retrieval applications.
Fuzzy prediction sets generalize binary predictions to include elements at varying confidence levels.
problem Binary prediction sets are limited; fuzzy prediction sets offer richer guarantees.
method Generalize prediction sets to fuzzy sets, showing they are e-values with merging properties.
result Optimal e-values lead to optimal fuzzy prediction sets, including optimal conformal prediction.
Paper defines kernels for fuzzy sets, useful in machine learning.
problem Uncertainty in data with fuzzy set membership.
method Defined cross product, intersection, non-singleton, and distance-based kernels.
result Kernels improve machine learning tasks with fuzzy data.
Enhances hashing for fast retrieval with correlated bits.
problem Fast retrieval and small memory footprint for large-scale information retrieval.
method Employing Boltzmann machine distribution as variational posterior to model correlations among hash code bits.
result Significant performance gains achieved by effectively modeling correlations among hash code bits.
Learning-based binary hashing has become a powerful paradigm for fast search and retrieval in massive databases. However, due to the requirement of discrete outputs for the hash functions, learning such functions is known to be very challenging. In addition, the objective functions adopted by existing hashing technique…
Music generation research has grown in popularity over the past decade, thanks to the deep learning revolution that has redefined the landscape of artificial intelligence. In this paper, we propose a novel approach to music generation inspired by musical segment concatenation methods and hash learning algorithms. Given…
Hyperplane hashing aims at rapidly searching nearest points to a hyperplane, and has shown practical impact in scaling up active learning with SVMs. Unfortunately, the existing randomized methods need long hash codes to achieve reasonable search accuracy and thus suffer from reduced search speed and large memory overhe…
MBGD-RDA trains TSK fuzzy systems efficiently on big datasets.
problem Training TSK fuzzy systems efficiently on big datasets.
method MBGD-RDA combines mini-batch gradient descent, regularization, AdaBound, and novel techniques for TSK fuzzy systems.
result MBGD-RDA achieves fast convergence and superior generalization on big datasets.
We propose theoretical and empirical improvements for two-stage hashing methods. We first provide a theoretical analysis on the quality of the binary codes and show that, under mild assumptions, a residual learning scheme can construct binary codes that fit any neighborhood structure with arbitrary accuracy. Secondly, …
Enhanced fuzzy system predicts chaotic time series with improved accuracy.
problem Forecasting chaotic time series with high uncertainty.
method Combines evolving fuzzy systems, participatory learning, KRLS, and type-2 fuzzy sets.
result Proposed model outperforms other methods in accuracy and complexity.
A new adaptive binarization technique using fuzzy integrals improves image quality.
problem Improving image thresholding quality.
method FLAT (Fuzzy Local Adaptive Thresholding) based on fuzzy integrals.
result The proposed FLAT method produces better image quality than traditional algorithms and neural networks.
Paper investigates differentiable fuzzy implications and their suitability for learning.
problem Analyzing differentiable fuzzy implications and their suitability for learning.
method Investigates the properties of fuzzy implications in a differentiable setting and introduces a new family of fuzzy implications.
result Various fuzzy implications are unsuitable for differentiable learning, and a new family of fuzzy implications is introduced.
SESSC clusters fuzzy rules for TSK classifiers, improving performance with label info.
problem Lack of supervised clustering for TSK fuzzy classifiers.
method SESSC integrates within-cluster compactness, between-cluster separation, and label information.
result SESSC initialization outperforms other clustering methods, especially for small rule numbers.
In supervised binary hashing, one wants to learn a function that maps a high-dimensional feature vector to a vector of binary codes, for application to fast image retrieval. This typically results in a difficult optimization problem, nonconvex and nonsmooth, because of the discrete variables involved. Much work has sim…
This paper explores fuzzy systems' equivalence to neural networks and other machine learning methods.
problem Designing optimal fuzzy systems and overcoming challenges.
method Comparative analysis of Takagi-Sugeno-Kang fuzzy systems with neural networks, mixture of experts, CART, and stacking ensemble regression.
result Functional equivalence between fuzzy systems and machine learning methods.
A new fuzzy clustering method using hyperbolic smoothing for large datasets.
problem Building fuzzy clusters for large data sets efficiently.
method A novel smoothing numerical approach to relax the sum-of-squares criterion, converting the problem into a differentiable optimization problem.
result The method produces better fuzzy partitions compared to traditional fuzzy C-means. In this paper we use fuzzy systems theory to convert the technical trading rules commonly used by stock practitioners into excess demand functions which are then used to drive the price dynamics. The technical trading rules are recorded in natural languages where fuzzy words and vague expressions abound. In Part I of t…
SDHR improves data hashing for better classification accuracy.
problem Efficient data hashing for high-dimensional data retrieval.
method Supervised Discrete Hashing with Relaxation (SDHR) using optimized regression targets.
result SDHR outperforms traditional methods in classification accuracy.
New method reduces complexity of fuzzy decision trees in Big Data.
problem Reducing complexity in multi-way fuzzy decision trees for Big Data classification.
method Two-step process: 1) Probability integral transform, 2) Ruspini strong fuzzy partition.
result Up to 6 million fewer leaves with similar classification accuracy.
Fuzzy ROC handles indeterminate data points in ROC analysis.
problem Handling indeterminate data points in ROC analysis.
method Extends ROC visualization to indeterminate data, addressing sensitivity and specificity bounds.
result Visual summarization of possibilities from different indeterminacy zones.
A new hashing method handles large-scale data with flexible similarity measures.
problem Efficient nearest neighbour search in large-scale systems with variable labels.
method End-to-end trainable network transforming data to uniform distribution on product of spheres, then hashing to binary form maximizing entropy.
result Outperforms baseline approaches in limited capacity regime.
A novel hash learning approach using codewords in Hamming space.
problem Hash learning for supervised, unsupervised, and semi-supervised scenarios.
method Uses codewords inferred from data to capture grouping aspects of hash codes, with regularization for automatic codeword selection. Solves via Block Coordinate Descent and SVM.
result Demonstrates superior performance in content-based image retrieval.
Hash codes are efficient data representations for coping with the ever growing amounts of data. In this paper, we introduce a random forest semantic hashing scheme that embeds tiny convolutional neural networks (CNN) into shallow random forests, with near-optimal information-theoretic code aggregation among trees. We s…
An attractive approach for fast search in image databases is binary hashing, where each high-dimensional, real-valued image is mapped onto a low-dimensional, binary vector and the search is done in this binary space. Finding the optimal hash function is difficult because it involves binary constraints, and most approac…
Unified probabilistic foundation for fuzzy simplicial sets in dimensionality reduction.
problem Lack of clear probabilistic interpretation in fuzzy simplicial sets.
method Introducing a probabilistic framework explaining fuzzy simplicial sets as marginals of probability measures on simplicial sets.
result Unified probabilistic theoretical foundation for fuzzy simplicial sets.
Fuzzy clustering reduces text data dimensions effectively.
problem Sparsity and high dimensionality in document-term frequency matrices.
method Fuzzy clustering applied as a new unsupervised feature transformation method.
result Fuzzy clustering outperforms PCA and SVD in reducing text data dimensions.
TSK-Streams learns fuzzy rules from data streams.
problem Adaptive learning from evolving data streams.
method Combines AMRules principles with fuzzy rule advantages.
result TSK-Streams performs highly competitively in experiments.
In this paper, we propose to (seamlessly) integrate b-bit minwise hashing with linear SVM to substantially improve the training (and testing) efficiency using much smaller memory, with essentially no loss of accuracy. Theoretically, we prove that the resemblance matrix, the minwise hashing matrix, and the b-bit minwise…
Proposes new random models for fuzzy clustering similarity measures.
problem Challenges in choosing a random model for fuzzy clustering similarity measures.
method Introduces three intuitive and explainable random models for fuzzy clusterings.
result Each random model has distinct behavior, emphasizing the importance of accurate model selection.
Optimized fuzzy entropy framework improves feature selection and classification performance.
problem Improving feature selection and classification in fuzzy entropy frameworks.
method Implemented and compared combinations of ideal vectors, maximal similarity classifiers, and fuzzy entropy functions.
result Optimized combination of ideal vector, similarity classifier, and fuzzy entropy function achieved the most stable performance for all three datasets.