In this task, you are given a part of an article. Your task is to generate headline (title) for this text. Preferred headlines are under fifteen words.
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Question: Multi-instance multi-label (MIML) learning is a challenging problem in many aspects. Such learning approaches might be useful for many medical diagnosis applications including breast cancer detection and classification. In this study subset of digiPATH dataset (whole slide digital breast cancer histopathology images) are used for training and evaluation of six state-ofthe-art MIML methods. At the end, performance comparison of these approaches are given by means of effective evaluation metrics. It is shown that MIML-kNN achieve the best performance that is %65.3 average precision, where most of other methods attain acceptable results as well.

Answer: Evaluation of Joint Multi-Instance Multi-Label Learning For Breast Cancer Diagnosis


Question: A totally semantic measure is presented which is able to calculate a similarity value between concept descriptions and also between concept description and individual or between individuals expressed in an expressive description logic. It is applicable on symbolic descriptions although it uses a numeric approach for the calculus. Considering that Description Logics stand as the theoretic framework for the ontological knowledge representation and reasoning, the proposed measure can be effectively used for agglomerative and divisional clustering task applied to the semantic web domain.

Answer: A Semantic Similarity Measure for Expressive Description Logics


Question: Phase retrieval problems involve solving linear equations, but with missing sign (or phase, for<lb>complex numbers) information. Over the last two decades, a popular generic empirical approach<lb>to the many variants of this problem has been one of alternating minimization; i.e. alternating<lb>between estimating the missing phase information, and the candidate solution. In this paper, we<lb>show that a simple alternating minimization algorithm geometrically converges to the solution<lb>of one such problem – finding a vector x from y,A, where y = |Ax| and |z| denotes a vector<lb>of element-wise magnitudes of z – under the assumption that A is Gaussian.<lb>Empirically, our algorithm performs similar to recently proposed convex techniques for this<lb>variant (which are based on “lifting” to a convex matrix problem) in sample complexity and<lb>robustness to noise. However, our algorithm is much more efficient and can scale to large<lb>problems. Analytically, we show geometric convergence to the solution, and sample complexity<lb>that is off by log factors from obvious lower bounds. We also establish close to optimal scaling<lb>for the case when the unknown vector is sparse. Our work represents the only known theoretical<lb>guarantee for alternating minimization for any variant of phase retrieval problems in the non-<lb>convex setting.

Answer:
Phase Retrieval using Alternating Minimization