9175149
Generation of Comprehensible Hypotheses from Gene Expression Data
Machine learning techniques have been recognized as powerful tools for the analysis of gene expression data. However, most learning techniques used in class prediction in gene expression analysis during the past years generate black-box models. Although the prediction accuracy of these models could be very well, they provide little insight into the biological facts. This paper holds the recognition that a more reasonable role for machine learning techniques is to generate hypotheses that can be verified or refined by human experts instead of making decisions for human experts. Based on this recognition, a general approach to generate comprehensible hypotheses from gene expression data is described and applied to human acute leukemias as a test case. The results demonstrate the feasibility of using machine learning techniques to help form hypotheses on the relationship between genes and certain diseases.
10964754
SETRED: Self-training with Editing
Self-training is a semi-supervised learning algorithm in which a learner keeps on labeling unlabeled examples and retraining itself on an enlarged labeled training set. Since the self-training process may erroneously label some unlabeled examples, sometimes the learned hypothesis does not perform well. In this paper, a new algorithm named Setred is proposed, which utilizes a specific data editing method to identify and remove the mislabeled examples from the self-labeled data. In detail, in each iteration of the self-training process, the local cut edge weight statistic is used to help estimate whether a newly labeled example is reliable or not, and only the reliable self-labeled examples are used to enlarge the labeled training set. Experiments show that the introduction of data editing is beneficial, and the learned hypotheses of Setred outperform those learned by the standard self-training algorithm.
32091631
K One-Way Heads Cannot Do String-Matching
We settle a conjecture raised by Z. Galil and J. Seiferas 14 years ago (J. Comput. System Sci.26(1983), 280?294):k-head one-way deterministic finite automata cannot perform string-machine (i.e., accept the language {x#y??u?vy=uxv}), for anyknonsensing heads.
22789743
New Applications of the Incompressibility Method
The incompressibility method is an elementary yet powerful proof technique based on Kolmogorov complexity [13]. We show that it is particularly suited to obtain average-case computational complexity lower bounds. Such lower bounds have been difficult to obtain in the past by other methods. In this paper we present four new results and also give four new proofs of known results to demonstrate the power and elegance of the new method.
20792301
An Efficient Algorithm for Computing Optimal Discrete Voltage Schedules
We consider the problem of job scheduling on a variable voltage processor with d discrete voltage/speed levels. We give an algorithm which constructs a minimum energy schedule for n jobs in O(dnlog n) time. Previous approaches solve this problem by first computing the optimal continuous solution in O(n3) time and then adjusting the speed to discrete levels. In our approach, the optimal discrete solution is characterized and computed directly from the inputs. We also show that O(nlog n) time is required, hence the algorithm is optimal for fixed d.
667096
k one-way heads cannot do string-matching
The string-matching problem is defined as follows [6]: Given a character string x, called the pattern, and a character string y, called the text, determine if x is a subword of y. It is well known that the string-matching problem is of great importance in practice, for example, the ``grep'' function in UNIX. Since the invention of the linear-time algorithms of [2, 3, 8], there have been constant efforts to find linear-time algorithms which use less space. See, e.g. [1]. The best theoretical result is in [6], where it was shown that string-matching can be performed by a six-head two-way deterministic finite automaton in linear time. That is, in linear time, such a machine can accept the language
12850359
Recognition of operator motions for real-time assistance using virtual fixtures
Hidden Markov Models (HMMs) are used for automatic segmentation and recognition of user motions. A new algorithm for real-time HMM recognition was developed. The segmentation results are used to provide appropriate assistance in a combined curve following and object avoidance task. This assistance takes the form of a virtual fixture, whose compliance can be altered online. Recognition and assistance experiments were performed using force and position data recorded from a cooperative manipulation system, where a robot and a human operator hold an instrument simultaneously. Recognition accuracy exceeds 90%, even when the users training the HMMs differ from those executing the task. For a task consisting of both path following and avoidance motions, an HMM-based virtual fixture switches the compliance from low to high when the user is trying to move away from the path. The HMM method improves operator performance in comparison with a constant virtual fixture and no virtual fixture.