Open intent classification is a challenging task in dialogue systems. On the one hand, it should ensure the quality of known intent identification. On the other hand, it needs to detect the open (unknown) intent without prior knowledge. Current models are limited in finding the appropriate decision boundary to balance the performances of both known intents and the open intent. In this paper, we propose a post-processing method to learn the adaptive decision boundary (ADB) for open intent classification. We first utilize the labeled known intent samples to pre-train the model. Then, we automatically learn the adaptive spherical decision boundary for each known class with the aid of well-trained features. Specifically, we propose a new loss function to balance both the empirical risk and the open space risk. Our method does not need open intent samples and is free from modifying the model architecture. Moreover, our approach is surprisingly insensitive with less labeled data and fewer known intents. Extensive experiments on three benchmark datasets show that our method yields significant improvements compared with the state-of-the-art methods. The codes are released at https://github.com/thuiar/Adaptive-Decision-Boundary.

Here we're going to look at an application of the k-nearest neighbours (kNN) algorithm to predict whether or not a telescope signal is gamma or hadron radiation using a Kaggle dataset. This is one of the older ones. I've just looked it up and the internet assures me that this was developed in the 1950s. It still works well today. I'll be using the scikit-learn kNN classification model for the example.

A plethora of results appear on a small google search "Logistic Regression". Sometimes it gets very confusing for beginners in data science, to get around the main idea behind logistic regression. And why wouldn't they be confused!!? Every different tutorial, article, or forum has a different narration on Logistic Regression (not including the legit verbose of textbooks because that would kill the entire purpose of these "quick sources" of mastery). Some sources claim it a "Classification algorithm" and some more sophisticated ones call it a "Regressor", however, the idea and utility remain unrevealed. Remember that Logistic regression is the basic building block of artificial neural networks and no/fallacious understanding of it could make it really difficult to understand the advanced formalisms of data science.

From exploratory data analysis to automated machine learning, look to these techniques to get your data science project moving — and to build better models.

The Linear Baseline model solves a convex problem and thus will converge to roughly the same optimum. This gives you basically an Idea for how informative naive correlation between features are. Logistic Regressions and Support Vector Machines are a common choice here in my experience. Regarding nonlinear models, the random forrest classifier is neat. XGBoost (Gradient Boosted Decision forrest) from the package of the same name is also realy good.

A machine learning guide on how to identify fraudulent credit card transactions by using the PyOD toolkit.

--Fabrication process variations can significantly influence the performance and yield of nano-scale electronic and photonic circuits. Stochastic spectral methods have achieved great success in quantifying the impact of process variations, but they suffer from the curse of dimensionality. Recently, low-rank tensor methods have been developed to mitigate this issue, but two fundamental challenges remain open: how to automatically determine the tensor rank and how to adaptively pick the informative simulation samples. This paper proposes a novel tensor regression method to address these two challenges. The resulting optimization problem can be efficiently solved via an alternating minimization solver . We also propose a two-stage adaptive sampling method to reduce the simulation cost. Our method considers both exploration and exploitation via the estimated V oronoi cell volume and nonlinearity measurement respectively. The proposed model is verified with synthetic and some realistic circuit benchmarks, on which our method can well capture the uncertainty caused by 19 to 100 random variables with only 100 to 600 simulation samples. Fabrication process variations (e.g., surface roughness of interconnects and photonic waveguide, and random doping effects of transistors) have been a major concern in nano-scale chip design. They can can significantly influence chip performance and decrease product yield [2]. Monte Carlo (MC) is one of the most popular methods o quantify the chip performance under uncertainty, but it requires a huge amount of computational cost [3]. Instead, stochastic spectral methods based on generalized polynomial chaos (gPC) [4] offer efficient solutions for fast uncertainty quantification by approximating a real uncertain circuit variable as a linear combination of some stochastic basis functions [5-7].

Textbook wisdom advocates for smooth function fits and implies that interpolation of noisy data should lead to poor generalization. A related heuristic is that fitting parameters should be fewer than measurements (Occam's Razor). Surprisingly, contemporary machine learning (ML) approaches, cf. deep nets (DNNs), generalize well despite interpolating noisy data. This may be understood via Statistically Consistent Interpolation (SCI), i.e. data interpolation techniques that generalize optimally for big data. In this article we elucidate SCI using the weighted interpolating nearest neighbors (wiNN) algorithm, which adds singular weight functions to kNN (k-nearest neighbors). This shows that data interpolation can be a valid ML strategy for big data. SCI clarifies the relation between two ways of modeling natural phenomena: the rationalist approach (strong priors) of theoretical physics with few parameters and the empiricist (weak priors) approach of modern ML with more parameters than data. SCI shows that the purely empirical approach can successfully predict. However data interpolation does not provide theoretical insights, and the training data requirements may be prohibitive. Complex animal brains are between these extremes, with many parameters, but modest training data, and with prior structure encoded in species-specific mesoscale circuitry. Thus, modern ML provides a distinct epistemological approach different both from physical theories and animal brains.

In this work, we address the problem of large-scale online face clustering: given a continuous stream of unknown faces, create a database grouping the incoming faces by their identity. The database must be updated every time a new face arrives. In addition, the solution must be efficient, accurate and scalable. For this purpose, we present an online gaussian mixture-based clustering method (OGMC). The key idea of this method is the proposal that an identity can be represented by more than just one distribution or cluster. Using feature vectors (f-vectors) extracted from the incoming faces, OGMC generates clusters that may be connected to others depending on their proximity and their robustness. Every time a cluster is updated with a new sample, its connections are also updated. With this approach, we reduce the dependency of the clustering process on the order and the size of the incoming data and we are able to deal with complex data distributions. Experimental results show that the proposed approach outperforms state-of-the-art clustering methods on large-scale face clustering benchmarks not only in accuracy, but also in efficiency and scalability.

Samples with ground truth labels may not always be available in numerous domains. While learning from crowdsourcing labels has been explored, existing models can still fail in the presence of sparse, unreliable, or diverging annotations. Co-teaching methods have shown promising improvements for computer vision problems with noisy labels by employing two classifiers trained on each others' confident samples in each batch. Inspired by the idea of separating confident and uncertain samples during the training process, we extend it for the crowdsourcing problem. Our model, CrowdTeacher, uses the idea that perturbation in the input space model can improve the robustness of the classifier for noisy labels. Treating crowdsourcing annotations as a source of noisy labeling, we perturb samples based on the certainty from the aggregated annotations. The perturbed samples are fed to a Co-teaching algorithm tuned to also accommodate smaller tabular data. We showcase the boost in predictive power attained using CrowdTeacher for both synthetic and real datasets across various label density settings. Our experiments reveal that our proposed approach beats baselines modeling individual annotations and then combining them, methods simultaneously learning a classifier and inferring truth labels, and the Co-teaching algorithm with aggregated labels through common truth inference methods.