A new method learns term weights for better text classification.
problem Improving text classification performance using term weighting.
method Learning to Weight (LTW) approach that optimizes term weights on training data.
result Learning to Weight (LTW) outperforms previous term weighting approaches in text classification.
A new term weighting scheme TF-IDFC-RF outperforms others in sentiment analysis.
problem Improving text classification in sentiment analysis.
method Proposes a novel supervised term weighting scheme TF-IDFC-RF and compares it with other schemes.
result TF-IDFC-RF outperforms all other schemes on two sentiment analysis datasets.
Improved cross-lingual document retrieval using regularized Wasserstein distance.
problem Cross-lingual document retrieval (CLDR) challenges.
method Extending Word Mover's Distance with term-weighting schemes and entropic regularization.
result Remarkable improvements in Mean Reciprocal Rank (MRR) compared to baselines.
Uniqueness found for elliptic equations with drift on manifolds.
problem Finding unique solutions to elliptic equations with drift on manifolds.
method Investigation in weighted Lebesgue spaces, focusing on conditions for uniqueness.
result Sharp conditions on drift term for uniqueness in polynomial volume growth manifolds.
We show that Chen-Ruan cohomology is a homotopy invariant in certain cases. We introduce the notion of a T-representation homotopy, which is a stringent form of homotopy under which Chen-Ruan cohomology is invariant. We show that while hyperkahler quotients of the cotangent bundle to a complex vector space by a circle …
WOCR combines orthogonal components with weighted regression for improved predictive performance.
problem Improving predictive performance in multiple linear regression.
method WOCR uses orthogonal components and weights based on correlations with the response.
result Enhanced predictive performance through weighted orthogonal components.
The paper evaluates different vector space models for text similarity.
problem Measuring semantic text similarity in natural language processing.
method Comparison of TFIDF, topic models, and neural models for patent-to-patent similarity.
result TFIDF performs well for longer, technical texts or finer distinctions.
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.
DEAM optimizes momentum weights dynamically to improve deep learning model training.
problem Errors in momentum weights propagate errors in optimization algorithms like ADAM.
method DEAM computes adaptive momentum weights based on discriminative angles, reducing hyperparameters and introducing a backtrack term.
result DEAM achieves faster convergence rates in both convex and non-convex deep learning model training.
SEF-M improves spiking neural network classification accuracy by 14%.
problem Improving spiking neural network classification accuracy.
method Meta-neuron based learning algorithm with time-varying weight model.
result Time-varying weight model improves classification accuracy by 14%.
The clustering ensemble technique aims to combine multiple clusterings into a probably better and more robust clustering and has been receiving an increasing attention in recent years. There are mainly two aspects of limitations in the existing clustering ensemble approaches. Firstly, many approaches lack the ability t…
WLDA enhances LDA for missing data, improving classification accuracy and interpretability.
problem Missing data in real-world datasets hinders classification accuracy and interpretability of LDA.
method WLDA incorporates a weight matrix to handle missing data directly, preserving interpretability.
result WLDA significantly outperforms traditional methods in datasets with missing values.
Develops new methods for risk-aware decision-making in medical bandits.
problem Risk-averse decision-making in medical contexts with limited data.
method Safe, anytime-valid concentration bounds, risk-aware contextual bandits, nonparametric algorithms.
result Improved decision-making algorithms for postoperative patient follow-up.
Novel approach reduces DNN redundancy and enhances computational efficiency.
problem Redundant parameters in large-scale DNNs pose hardware deployment challenges.
method WGSEF regularization technique for structured sparsity.
result Reduces redundancy and enhances computational efficiency.
New metrics improve evaluation of EEG event detection algorithms.
problem Lack of standard evaluation metrics for EEG event detection.
method Proposed and demonstrated new metrics: ATWV and TAES.
result Deep learning algorithms need improvement for strict user acceptance.
Poisson variational autoencoders introduce a metabolic cost term that penalizes high baseline activity.
problem Energy constraints in computation.
method Poisson variational autoencoders with a Kullback-Leibler divergence term proportional to firing rates.
result Poisson variational autoencoders introduce a metabolic cost term that penalizes high baseline activity.