Exact Convex Confidence-Weighted Learning
نویسندگان
چکیده
Confidence-weighted (CW) learning [6], an online learning method for linear clas-sifiers, maintains a Gaussian distributions over weight vectors, with a covariancematrix that represents uncertainty about weights and correlations. Confidenceconstraints ensure that a weight vector drawn from the hypothesis distributioncorrectly classifies examples with a specified probability. Within this framework,we derive a new convex form of the constraint and analyze it in the mistake boundmodel. Empirical evaluation with both synthetic and text data shows our version ofCW learning achieves lower cumulative and out-of-sample errors than commonlyused first-order and second-order online methods.
منابع مشابه
Multi-Class Confidence Weighted Algorithms
The recently introduced online confidence-weighted (CW) learning algorithm for binary classification performs well on many binary NLP tasks. However, for multi-class problems CW learning updates and inference cannot be computed analytically or solved as convex optimization problems as they are in the binary case. We derive learning algorithms for the multi-class CW setting and provide extensive...
متن کاملExact Soft Confidence-Weighted Learning
In this paper, we propose a new Soft Confidence-Weighted (SCW) online learning scheme, which enables the conventional confidence-weighted learning method to handle non-separable cases. Unlike the previous confidence-weighted learning algorithms, the proposed soft confidence-weighted learning method enjoys all the four salient properties: (i) large margin training, (ii) confidence weighting, (ii...
متن کاملRe-revisiting Learning on Hypergraphs: Confidence Interval and Subgradient Method
We revisit semi-supervised learning on hypergraphs. Same as previous approaches, our method uses a convex program whose objective function is not everywhere differentiable. We exploit the non-uniqueness of the optimal solutions, and consider confidence intervals which give the exact ranges that unlabeled vertices take in any optimal solution. Moreover, we give a much simpler approach for solvin...
متن کاملWeighted composition operators between growth spaces on circular and strictly convex domain
Let $Omega_X$ be a bounded, circular and strictly convex domain of a Banach space $X$ and $mathcal{H}(Omega_X)$ denote the space of all holomorphic functions defined on $Omega_X$. The growth space $mathcal{A}^omega(Omega_X)$ is the space of all $finmathcal{H}(Omega_X)$ for which $$|f(x)|leqslant C omega(r_{Omega_X}(x)),quad xin Omega_X,$$ for some constant $C>0$, whenever $r_{Omega_X}$ is the M...
متن کاملConvex Hull and Voronoi Diagram of Additively Weighted Points
We provide a complete description of dynamic algorithms for constructing convex hulls and Voronoi diagrams of additively weighted points of R. We present simple algorithms and provide a complete description of all the predicates. The algorithms have been implemented in R and experimental results are reported. Our implementation follows the CGAL design and, in particular, is made both robust and...
متن کامل